Oximetry system for detecting ventilation instability

ABSTRACT

A system for detecting ventilation instability includes a pulse oximeter for generating a time series of oxygen saturation values and a processor programmed to detect the occurrence of patterns along the time series of clustered variations indicative of ventilation instability. An output is provided in response to the detection of the occurrence.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.60/052,438, filed Jul. 14, 1997, the contents of which are herebyincorporated herein by reference and the benefit of U.S. ProvisionalApplication No. 60/052,439, filed Jul. 14, 1997, the contents of whichare hereby incorporated herein by reference.

This application is a continuation of U.S. application Ser. No.09/115,226, filed Jul. 14, 1998, now U.S. Pat. No. 6,223,064, which is acontinuation-in-part of U.S. application Ser. No. 08/789,460, filed Jan.27, 1997, now U.S. Pat. No. 5,891,023, which is a continuation of U.S.patent application Ser. No. 08/391,811, filed Feb. 21, 1995, now U.S.Pat. No. 5,605,151, which is a continuation of U.S. patent applicationSer. No. 08/151,901 filed Nov. 15, 1993, now U.S. Pat. No. 5,398,682,which is a continuation-in-part of U.S. patent application Ser. No.08/931,976, filed Sep. 17, 1997, now U.S. Pat. No. 5,916,221. Thecontents of application Ser. Nos. 08/789,460, 08/391,811, 08/151,901,08/931,976, 09/115,226, and PCT/US93/97726, and of U.S. Pat. Nos.5,891,023; 6,223,064; 5,605,151; and 5,398,682 are all herebyincorporated herein by reference.

BACKGROUND AND SUMMARY OF THE INVENTION

Obstructive Sleep Apnea is now recognized as one of the most commondisorders in the US. The lower oxygen levels associated with ObstructiveSleep Apnea is now known to be a major cause of cardiovascular morbidityincluding heart attack and stroke. A crisis exists in the US in thattraditional expensive polysomnography cannot be used to identify thesepatients on a sufficient scale. The situation is analogous to having adisease as common and subtle as insulin dependent diabetes without aninexpensive and widely implementable and simple mechanism to diagnosethe disorder (such as exists for diabetes). Millions of patients remainundiagnosed. The development of a diagnostic system which can allowsimplified diagnosis of obstructive sleep apnea by the primary carephysician is a national healthcare priority of substantial scale. Theprevention of hundreds of thousands of annual excess deaths, stroke andheart attacks associated with obstructive sleep apnea through simplifiedrecognition of this disorder is the most important purpose of thepresent invention. These excess deaths are occurring annually in a greatpart due to the lack of availability of this technology resulting in avast pool of undiagnosed cases of Sleep Apnea. Despite the fact thatobstructive sleep apnea is easily treated, both the patient and thefamily are often completely unaware of the presence of this dangerousdisease, thinking the patient just a “heavy snorer”.

Obstructive sleep apnea often develops insidiously as a patient entersmiddle age and begins to snore. The major cause is an increase in fatdeposition (often age related) in the neck which results in narrowing ofthe airway. (In fact the probability that a 40 year old has sleep apneais directly related to his or her neck circumference). When the muscletone of the upper airway diminishes during sleep and negative pressureassociated with inspiration through this somewhat narrow airway resultsin collapse of the upper airway in a manner analogous to the collapse ofa cellophane straw. This results in airway obstruction and, effectivelychokes off all air movement. The choking patient (still asleep) beginsto struggle and inhales more forcibly, thereby, further lowering upperairway pressure and causing further collapse of the upper airway. Duringthis time, substantially no air movement into the chest occurs and thepatient experiences a progressively fall in oxygen (similar to the falloccurring early in drowning). The fall in oxygen produce central nervoussystem stimulation contributing to hypertension and potential heart andblood vessel injury and finally results in arousal. Upon arousal,increase in airway muscle tone opens the airways and the patient rapidlyinhales and ventilates quickly to correct the low oxygen levels.Generally, the arousal is brief and the patient is not aware of thearousal (or of the choking since this occurs during sleep). Once oxygenlevels have been restored, the patient begins again to sleep moredeeply, upper airway tone again diminishes, the upper airway collapsesand the cycle is repeated stressing the heart with low oxygen in arepetitive fashion. Often this repeating cycle over many yearseventually results in damage to the heart muscle and/or the coronaryarteries. As the patient ages, the consequences of undiagnosedobstructive sleep apnea is often either a progressive decline in heartmuscle function (and eventual heart failure) or heart infarction.

The duration and severity of each apnea is quite variable from patientto patient and with the same patient throughout the night. Indeed, thedisease process represents a spectrum of severity from mild snoring,which is associated with incomplete and inconsequential airwayobstruction, to severe apneas which can result in fatal hypoxemia.

This disease commonly results in excessive daytime sleepiness and candisrupt cognitive function during the day due to fragmentation of sleepduring the night associated with recurrent arousals of which the patientis not aware.

Although this disease commonly affects obese patients, it may occur inpatients with any body habitus. Because this disease is so common andbecause it presents with the subtle and common symptoms of excessivedaytime sleepiness, morning headache, and decreasing ability toconcentrate during the day, it is critical that an inexpensive techniquefor accurately diagnosing and treating this disease be developed.Traditionally, this disease has been diagnosed utilizing a complex andexpensive multi-channel polysomnogram. This is generally performed in asleep lab and involves the continuous and simultaneous measurement andrecording of an encephalogram, electromyogram, extraoculogram, chestwall plethysmogram, electrocardiogram, measurements of nasal and oralair flow, and pulse oximetry. These, and often other, channels aremeasured simultaneously throughout the night and these complexrecordings are then analyzed to determine the presence or absence ofsleep apnea.

The problem with this traditional approach is that such complex sleeptesting costs between one-thousand to thirty five hundred dollars. Sincesleep apnea is so common, the cost of diagnosing obstructive sleep apneain every patient having this disease in the United States would exceedTen Billion Dollars. It is critical that a new, inexpensive technique ofaccurately diagnosing sleep apnea be developed.

Nocturnal oximetry alone has been used as a screening tool to screenpatients with symptoms suggestive of sleep apnea to identify whether ornot oxygen desaturations of hemoglobin occur. Microprocessors have beenused to summarize nocturnal oximetry recordings and to calculate thepercentage of time spent below certain values of oxygen saturationHowever, oxygen desaturation of hemoglobin can be caused by artifact,hypoventilation, ventilation perfusion mismatching. For these reasons,such desaturations identified on nocturnal oximetry are not specific forsleep apnea and the diagnosis of sleep apnea has generally requiredexpensive formal polysomnography.

The present invention comprises a system and technique for deriving andutilizing the analysis of graphical pulse oximetry-derived waveforms asa function of time to accurately diagnosis sleep apnea with adequatespecificity to, in many cases, eliminate the need for expensive formalpolysomnography.

It is the purpose of this invention to provide an inexpensive system forthe collection and analysis of pulse oximetry values as a function oftime during sleep to provide a diagnosis of sleep apnea with a highdegree of specificity.

This invention provides a reliable and specific means for the diagnosisof obstructive sleep apnea which can be performed in the patient's homewithout attendance of technical personnel. It is further the purpose ofthis invention to provide, an inexpensive and accurate means to bothscreen for and specifically diagnose obstructive sleep apnea by a singleovernight recording in the patient's home without the need for multipleconnections to different parts of the patient's body. It is further thepurpose of this invention to define a technique for diagnosingobstructive sleep apnea utilizing the calculation of the ascending anddescending slope ratio of phasic oxygen desaturations measured duringsleep.

Specifically, the present invention defines a device for diagnosingsleep apnea, that has the following components. First, a means mustdetermine an oxygen saturation of a patient's blood. This saturationvalue is coupled to a means for identifying a desaturation event basedon the saturation value. The desaturation event is one in which saidoxygen saturation falls below a baseline level by a predetermined amountand for a predetermined time. The slope of the event is calculated bymeans for calculating a slope of said desaturation event representing arate of change per unit time of fall of oxygen saturation. This slope isused by a means for comparing said calculated slope with a value ofslope which is determined in advance to be indicative of sleep apnea,and determination of diagnosis of sleep apnea is made based on saidcomparing.

The comparing can be done by:

1) comparing with an absolute number which is likely to indicate a sleepapnea, or

2). comparing with other slopes taken at different times.

The identifying means can also identify a resaturation, immediatelyfollowing said desaturation and coupled with said desaturation, in whichthe oxygen saturation rises, and wherein the determination can also bebased on a slope of said resaturation.

Many other ways of calculating the slope are also disclosed herein.

These and other aspects of the invention will now be described in detailwith reference to the accompanying drawings, wherein:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a block diagram of the basic system of the presentinvention;

FIG. 2 shows a basic flowchart of operation of the present invention;

FIGS. 3 and 4 show basic desaturation events and many of the parametersassociated therewith;

FIG. 5 shows a specific way in which a comparison can utilize thecalculation of the area above each desaturation event compared to areaabove each coupled resaturation event.

FIG. 6 shows a slope cluster complex plotted as slope against time.

FIG. 7 shows a slope cluster complex plotted as oxygen saturationagainst time.

FIG. 8 shows the power spectrum of the signal segment of FIG. 7.

FIG. 9 shows a time domain signal segment from an OSA disease freepatient with a noisy baseline.

FIG. 10 shows the power spectrum of the signal segment of FIG. 9.

FIG. 11 shows a plot comparing disease free patients with patientshaving a diagnosis of obstructive sleep apnea.

FIG. 12 shows one preferred frequency domain algorithm for screeningpatients with obstructive sleep apnea.

FIG. 13 shows one preferred combined time and frequency domain algorithmfor diagnosing patients with obstructive sleep apnea.

FIG. 14 shows a schematic illustration of the sequential building ofobjects according to the present invention.

FIGS. 15a and 15 b is one preferred listing of an object orientedprogramming code according to the present invention.

FIG. 16 is a schematic illustration of one preferred embodiment of aportable minute volume indexing pulse oximeter according to the presentinvention.

FIG. 17 is a schematic illustration of a spirometer coupled to a pulseoximeter for generating a ventilation indexed pulse oximetry valueaccording to the present invention.

FIG. 18 is a schematic diagram of a microprocessor for coupling with aspirometer and a pulse oximeter for generating a ventilation indexedpulse oximetry value according to the present invention.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The inventor of the present invention found, relative to sleep apneadiagnosis, that the waveform pattern of oximetry during a sleeprecording can be considered in relation to the physiologic parameterswhich affect oxygen saturation over time. Specifically, during an apneicperiod, arterial oxygen saturation initially falls as a function of theoxygen saturation of mixed venous blood and oxygen uptake from residualexchangeable oxygen within the lungs. Subsequently, arterial oxygensaturation falls directly as a function of oxygen consumption and globaloxygen stores. These stores of oxygen are very limited. The sources ofoxygen available during an apneic period include residual exchangeableoxygen within alveoli and airways, the oxygen bound to hemoglobin,dissolved oxygen within body tissues and oxygen stored as myoglobin.These stores are rapidly depleted during an apneic period as a functionof global oxygen consumption. As oxygen stores are depleted, thecellular oxygen levels fall, and mixed venous oxygen saturationprogressively diminishes. Since a small amount of exchangeable oxygensupply exists within alveoli and airways, arterial oxygen saturation, asmeasured by the pulse oximeter is briefly unaffected by the initial fallin body oxygen storage. However, since oxygen stores within the alveoliare extremely limited, arterial oxygen saturation then progressivelyfalls toward that of mixed venous arterial blood saturation since littlesignificant gas exchange occurs as mixed venous blood passes byessentially unventilated alveoli. The partial pressure of oxygen inarterial blood therefore progressively falls toward the mean partialpressure of oxygen in body tissues at the cellular level.

It is possible to measure indirectly the partial pressure of oxygen inarterial blood by measurement of arterial oxygen saturation ofhemoglobin utilizing a pulse oximeter 12. If the probe 13 of pulseoximeter is placed on a patient's finger or other body part during aprolonged apneic period, a progressive decrement in arterial saturationwill be identified as a function of the fall in arterial oxygen partialpressure. Although the initial decline in arterial oxygen saturation isgreatly dependent on mixed venous oxygen saturation, since body oxygenstores during a apnea cannot be repleted, the subsequent portion of thefall in arterial oxygen saturation as measured by a pulse oximeter overtime will be directly correlated to the oxygen consumption of thepatient. The average oxygen consumption of a resting human (approx. 3.5ml/kg/min) has a relatively constant relationship to average bodyarterial oxygen stores (approx. 25 ml/kg). Although substantialvariability exists in body oxygen stores in chronically ill patientswith low cardiac output states (resulting in lower mixed venous oxygenstorage), a finite range of oxygen stores exists. Indeed, even in thepresence of severe compensated disease, mixed venous oxygen saturationgenerally ranges from 50%-80%. Therefore, a sleeping human has adefinable and predictable range of slopes of arterial oxygen saturationdecrement as a function of the baseline mixed venous oxygen saturationinitially and of oxygen consumption and body oxygen stores terminally.Although augmented body muscular activity associated with obstructiveapnea could modestly increase oxygen consumption and although a decreasein oxygen consumption may occur below a critical levels of tissueoxygenation, the declining range of slope of desaturation is stillpredictable within only modest variances.

To understand the predictable parameters of arterial pulse oximetrywaveform, it is important to consider the way in which pulse oximetryreflects total body oxygen stores. Total body oxygen stores can beconceived as representing four major compartments:

1. The Lung Compartment,

2. The Arterial Compartment,

3. The Tissue Compartment, and

4. The Venous Compartment.

Oxygen enters the lungs and is stored sequentially in each of thesecompartments. When oxygen is depleted during apnea, depletion occursfirst in the tissue compartment, second in the venous compartment, thirdin the lung compartment, and fourth in the arterial compartment.Whereas, when oxygen is repleted, oxygen appears first in the lungcompartment, second in the arterial compartment, third in the lungcompartment, and fourth in the venous compartment. It can be seen,therefore, that since pulse oximetry measurements reflect oxygen storedwithin the arterial compartment, if sequential depletion of arterialsaturation occurred due to phasic apneas that the initial apneic episodewould result in depletion of the arterial compartment only after thesubstantial depletion of other compartments has developed.

Using the above, the inventor of the present invention realized that hecould predict with reasonable certainty whether or not a desaturationoccurring during a continuous nocturnal oximetry measurement fallswithin the anticipated range of parameters which define the slope ofarterial oxygen desaturation of hemoglobin which can physiologicallyoccur during an apneic episode. In this manner, each desaturationepisode can be defined, as a function of the characteristics of thewaveform of deflection, as either consistent with an apneic episode orinconsistent with an apneic episode. Saturations which decrease toorapidly to be accounted for on the basis of physiologic oxygen depletiondue to apnea would be identified as inconsistent with an apneic episodeand therefore identified as being secondary to artifact. On the otherhand, the desaturation episodes which decrease too slowly to beaccounted for on the basis of physiologic oxygen depletion and would beidentified as inconsistent with an apneic episode and thereforesecondary to either hypoventilation, alterations in ventilationperfusion matching, or to artifact. The means for identifying adesaturation event is preferably a processor; and according to the firstembodiment of this invention, as described above, the processor comparesa calculated slope of the event with a value of slope which isdetermined in advance to be indicative of sleep apnea. A diagnosis ofsleep apnea is made based on that comparison.

More specifically, the preferred embodiment of the sleep apnea diagnosissystem 10 of the present invention is shown in FIG. 1. It includes aconventional pulse oximeter (12) with a probe (14) for transilluminationor reflection from a human body part such as a finger (16). The oximeteris connected to a microprocessor (20) which records oxygen saturationand pulse as a function of time. A printer (24) is connected to themicroprocessor. The microprocessor analyzes the oxygen saturation valuesas a function of time, as will be discussed in detail herein. In onepreferred embodiment, the system is used in the following way:

The microprocessor is disposed in connection with the oximeter with aprobe and printer for recording the oxygen saturation as a function oftime, and the oximeter probe is attached to a patient. The oxygensaturation of hemoglobin is recorded as a function of time while thepatient sleeps.

A measurement interval of, for example, 10 minutes is defined along thesleep recording as shown in step 200 of FIG. 2. Step 202 defines a meanmaximum baseline range of oxygen saturation of hemoglobin (±3%saturation) is defined over the measurement interval.

A desaturation event can be defined as at least a 4% substantiallyuninterrupted decrement in saturation below the defined baseline mean ofoxygen saturation. A lower percentage can be used to increasesensitivity. Each desaturation event is identified in step 204, and thedesaturation change of each desaturation event is measured. Thedesaturation interval is defined as the duration of the uninterrupteddecline in saturation of each desaturation event.

Then, slopes are calculated. The descending slope of each desaturationevent is calculated as:

ΔS_(D)/ΔT_(D)

where:

ΔS_(D)=Desaturation change (in % saturation;

ΔT_(D)=Desaturation interval (in seconds).

A resaturation event is defined as a substantially uninterrupted rise insaturation which terminates the declining slope of the desaturationevent. The resaturation change of each resaturation event is alsomeasured.

The resaturation interval is measured as the duration of theuninterrupted rise in saturation of each resaturation event. Theascending slope of each resaturation event is calculated as:

ΔS_(R)/ΔT_(R).

where:

ΔS_(R)=Resaturation change (in % saturation);

ΔT_(R)=Resaturation interval in seconds.

A phasic desaturation event is defined using all coupled desaturationand resaturation events wherein the sum of the duration of thedesaturation event and the resaturation event is less than 3.5 minutesand wherein the descending slope falls within a finite range of between1.3%/sec and 0.3%/sec.

The descending to ascending saturation slope ratio of each phasicdesaturation event is calculated as:

(ΔS_(D)/ΔT_(D))/(ΔS_(R)/ΔT_(R)).

The number of probable apneic events within the measurement interval isdefined as the number of phasic desaturation events falling within thefinite range of ascending to descending slope ratios of between3.5-10.5.

Each probable apneic event is marked with the identity marker, PA, andthe above steps are repeated for each additional 10 min. interval alongthe recording for the entire sleep recording.

Then, appropriate action is taken: either the pulse oximetry waveform isprinted as a function of time with each probable apneic event marked PAfor identification, or treatment of sleep apnea is either manually orautomatically administered.

The probability that a patient has sleep apnea will be a direct functionof the number of phasic desaturations which meet the above criteria forsleep apnea per hour of recording and this probability can be calculatedand printed.

Therefore, in the preferred embodiment, each desaturation event isidentified as to whether or not it meets the criteria for physiologicapnea. The number of events per hour are then calculated and thenprinted. Each desaturation event which has been identified by themicroprocessor as consistent with a physiologic apnea is so marked (suchas PA for probable apnea or PCA for physiologically consistent withapnea). The pulse oximetry waveform in the preferred embodiment is thenprinted to provide a hard copy. This printed hard copy includesidentification of each desaturation event which has been determined bythe microprocessor as consistent with a physiologic apnea. In addition,the presence of desaturation slope acceleration, as will be discussed,by comparing closely spaced consecutive desaturation slopes as in FIG. 4and such identification also provided on the printed hard copy.

This invention therefore provides a compact, single device which iseasily suitable for home use and can be simply taken home by the patientand interfaced with a body part, such as a finger, to provide bothscreening and a mechanism to provide a specific diagnosis of sleep apneawith a single overnight recording. The hard printed copy providesgraphical data which can be overread by the physician since the computerspecifically identifies the desaturation events which have beeninterpreted as consistent with sleep apnea. This provides the physicianwith the opportunity to determine whether he or she agrees with thediagnostic interpretation of the microprocessor.

The diagnosis can be treated by repeating the sleep recording duringnasal CPAP (Continuous Positive Airway Pressure) therapy. Theidentification of multiple desaturations with patterns as defined abovewhich are consistent with the physiology of apnea and which areeliminated by nasal CPAP therapy is diagnostic of apnea and furtherestablishes the parameters defining effective treatment requirements.

The invention includes the system taking additional action based on theidentification of the diagnosis of sleep apnea based on the above slopecomparison. The action can include, as in FIG. 1, the microprocessoractivating a range of nasal continuous airway pressures through apressure controller within defined limits to automatically andeffectively treat a patient's sleep apnea once the diagnosis of sleepapnea has been made by the microprocessor. Activation of flow isinitiated by the microprocessor on identification of multiple sleepapnea-related desaturations meeting the criteria as descried above. Thepressure can be titrated upward by, for example 1-2 cm H₂O pressureincrements by the microprocessor upon identification of multipleconsecutive desaturations which are not effectively eliminated by thestarting pressure.

In this way, the invention greatly enhances the diagnostic sensitivityand specificity of nocturnal oximetry in the diagnosis of sleep apneaand to further utilize the identification of oximetry-deriveddesaturation events to trigger the storage and/or collection ofadditional sensory data concerning each desaturation event and;furthermore, the system can be utilized to automatically initiate andadjust therapy to mitigate further after following desaturation events.

In addition to a definable descending desaturation slope, oximetrymeasurements during apnea periods have other definable and predictableparameters. Importantly, apneic episodes have a definable andpredictable range of duration. It is clear that brief apneic episodes,for example with brief breath holding does not result in significantarterial oxygen desaturation as measured by pulse oximetry. However,when apneic periods are prolonged as with obstructive sleep apnea,oxygen desaturation progressively declines as a function of factors, aspreviously discussed. Unless such an apneic episode is limited induration, the patient would die from hypoxemia. Therefore, eachdesaturation which occurs as a function of apnea will have a phasicquality with a predictable range of duration. A second aspect of theinvention analyzes the duration of the apneic episode to determine if itis of a duration likely to indicate sleep apnea.

The range of duration generally does not exceed three minutes.Therefore, for a desaturation event identified by pulse oximetry to besecondary to an apneic episode, it should preferably have a duration ofless than three minutes. Oxygen desaturations due to sleep apnea shouldbe terminated with the resaturation of recovery within 3-3.5 minutes orless. Oxygen desaturation events which occur for greater than threeminutes are identified as either secondary to hypoventilation,ventilation perfusion mismatching, or artifact.

Another aspect of the invention is based on the recognition that anapneic episode which occurs during sleep is generally reversed by anarousal. At this point, the patient's central nervous system increasesupper airway tone and atmospheric gas rapidly enters the lungs andexchanges with the oxygen depleted gas within the alveoli. This exchangeoccurs within a few seconds. Since mixed venous blood in pulmonarycapillaries rapidly equilibrates with the partial pressure of oxygen inthe alveoli, arterial oxygenation will recover within seconds of therepletion of oxygen within alveoli. It should be noted that the amountof time required for blood to pass from the pulmonary capillaries to theperipheral site of pulse oximetry measurement can be measured is verybrief. Therefore, the ascending slope of oxygen saturation duringrecovery from an apneic episode is extremely rapid. Ascending slopeswhich are not rapid are unlikely to be secondary to repletion of oxygenpartial pressure within alveoli associated with arousal from an apneicepisode and rather may be secondary to a crescendo of increasingrespirations following a hypoventilation episode as in Cheyne-Stokesrespirations or may be secondary to improvement in ventilation perfusionmatching. In a recent study performed by the present inventor the meanslope of desaturation was 0.8% per second, with all desaturation slopesranging between 0.3% per second and 1.1% per second. The mean slope ofrecovery 7.6% per second, with recovery slopes ranging from 2.5% persecond to 8.3% per second. The mean recovery to apnea slope ratio was7.66, with a range of 3.8 to 10.4. Hence, in yet another aspect of theinvention, the resaturation slope, immediately following thedesaturation, is also determined, and used in the diagnosis of sleepapnea.

Additional ways of comparing the calculated slope with a value of slopewhich is determined in advance to be indicative of sleep apnea includeusing other parameters to enhance the specificity of continuousnocturnal oximetry in the diagnosis of sleep apnea include comparisonsof consecutive desaturation slope values and the identification ofalterations in desaturation values as a function of events occurringimmediately prior to the desaturation event.

Since obstructive sleep apnea events occur by similar physiologicprocess each time within the same patient, consecutive desaturationevents will commonly have similar desaturation slopes. Theidentification of these consecutive desaturation events having similardesaturation slopes which have values consistent with physiologic apneaprovides additional evidence supporting these events as secondary tocyclic obstructive sleep apnea.

Furthermore, the preceding desaturation event can effect the shape andthe slope of the desaturation event which immediately follows. That is,preceding desaturation event may accelerate the initial portion of theslope of the following desaturation. Although other factors maycontribute to the development of this increase in desaturation slope,the primary factor appears to be the depletion of body oxygen storeswhere insufficient time has developed for repletion for tissue andvenous oxygen stores. In other words, during rapidly cycling apneicevents, recovery time may be inadequate to replete all body oxygenstores. However, the pulse oximeter is measuring arteria oxygensaturation. Therefore, after repletion of oxygen stores within the lung,arterial oxygen saturation rapidly rises before venous oxygen storeshave been repleted. If an apneic event recurs before the restoration ofvenous oxygen stores, this apneic event will be superimposed uponsubstantially depleted total body oxygen stores despite the fact thatpulse oximetry may demonstrate normal arterial oxygen saturation. Sinceat this time apnea is occurring in the presence of markedly depletedbody oxygen stores (i.e. a much lower mixed venous oxygen saturation),the initial portion of the slope of the declining arterial oxygensaturation may be substantially greater than the slope of the decline ofoxygen saturation which occurred during the preceding desaturationevent. This phenomenon would not be expected to occur in associationwith artifact and would only be expected to occur in the presence ofrapidly cycling changes in body tissue oxygen stores. Consecutiveclosely spaced desaturation events, therefore, interact so that thefirst desaturation event can affect the waveform of the seconddesaturation event provided the interval between the two events is shortenough and the level of desaturation occurring in the first event issubstantial enough to result in a sizable depletion of total body oxygenstores.

The greatest portion of oxygen storage is within the venous compartment.At any given time, therefore, the amount of global oxygen stored is, inlarge part, a function of the extent of excess of oxygen delivered tothe tissues which is stored within the venous pool. In the absence ofarterial hypoxemia or profoundly compromised cardiovascular function,oxygen delivery substantially exceeds oxygen demand; resulting inconsiderable oxygen stores within the mixed venous pool. The amount ofoxygen stored within the mixed venous pool can, therefore, be seen as adynamically-stored, hidden buffer which mitigates the decline insaturation attendant any change in alveolar ventilation. Althoughpatients with profoundly decreased mixed venous oxygen saturations wouldbe expected to have a more rapid and greater fall in arterial oxygensaturation for any given level of change in alveolar ventilation, thisstill falls within a definable range.

During very rapidly cycling apneas (i.e. apneas occurring within lessthan 10-20 seconds of each other), body oxygen stores can be seentherefore as a moving wave through consecutive body compartments whereinthe first wave affects the configuration of the second wave. Theidentification of this effect should be virtually diagnostic of rapidlycycling sleep apnea and this phenomenon can be exploited to assist inthe specific diagnosis of sleep apnea utilizing the recording ofnocturnal oximetry alone.

Desaturation slope acceleration may occur when cyclic apneic eventsoccur within less than 10 seconds of each other and when the depth ofarterial saturation associated with the first cyclic event is greaterthan 15%. The inter-desaturation event intervals can be definedspecifically as that point wherein the first desaturation event recoverssubstantially to baseline to the point in time when the seconddesaturation event begins to decline from the baseline.

It can be seen, therefore, that a declining waveform of arterial oxygendesaturation in severe sleep apnea can be expected to have two majorphysiologically-derived components: 1) the slope of the initialdeclining limb which is primarily a function of the level of mixedvenous oxygen saturation at the onset of apnea and the amount ofexchangeable oxygen in the lung remaining after the onset of apnea. 2)the second component or terminal limb is primarily a function of globaloxygen consumption relative to body oxygen stores. (The terminal limbmay not be present if apnea is brief.) The slope of the initial andterminal limb are generally similar in patients with normal mixed venousoxygen saturations. However, in patients with significantly low mixedvenous oxygen saturation, the initial limb may have a much greater slopethan the terminal limb, producing an angled appearance suggestingantecedent depletion of mixed venous oxygen stores.

The magnitude of the oxygen deficit which is derived from the precedingapneic event less the intervening excess oxygen uptake which attenuatesthis deficit between the apneas defines the magnitude of the slopeacceleration of the initial limb of the after-following desaturationevent. Therefore, an interval of oxygen deficit is present following asustained apnea but it is hidden since arterial oxygen saturation isnormal.

FIG. 3 illustrates a desaturation event and many of the parameters asdiscussed supra which define the event. The parameters shown include:

ΔS_(D) Fall in saturation (in % sat.)

ΔS_(R) Rise in saturation (in % sat.)

ΔT_(D) Duration of the fall in Saturation/desaturation (in seconds)

ΔT_(R) Duration of the rise in saturation/resaturation (in seconds)

M_(D)=ΔS_(D)/ΔT_(D)=Mean Slope of Desaturation

M_(R)=ΔS_(R)/ΔT_(R)=Mean Slope of Resaturation.

We also define the following terms:

AI The apnea interval—(the actual time wherein the patient experiencescessation of airflow which precipitates oxygen desaturation.)

OAI The occult apnea interval—(the interval wherein apnea has occurred;however, arterial oxygen stores are maintained by a shift of oxygenstores form the lung and venous compartment into the arterialcompartment hiding the fall in body oxygen stores with respect to theoximetry measurement.)

OODI The occult oxygen deficit interval—(the interval immediatelyfollowing return of oxygen saturation to near baseline after adesaturation event and wherein mixed venous oxygen desaturationpersists. If a second apnea occurs within this interval, the slope ofdesaturation may be increased).

Using these parameters and realizations discussed supra, the inventor ofthe present invention made a system and technique which automaticallyanalyzed the waveform pattern of continuous nocturnal oximetry, tospecifically identify the presence or absence of moderate to severeobstructive sleep apnea-induced arterial oxygen desaturation. Such asystem and technique makes it possible to diagnose moderate to severeobstructive sleep apnea with confidence with a single channel recordingof nocturnal oximetry alone avoiding the need for complex and expensivepolysomnography in the diagnosis of this disorder. The system andtechnique includes a mechanism to achieve the measurement of acompendium of parameters which are repetitively measured and analyzed,each improving the specificity of the diagnosis.

A summary of one such technique is as follows:

1. Dispose a microprocessor in connection with the oximeter with a probeand printer for recording the oxygen saturation of hemoglobin as afunction of time.

2. Attach the oximeter probe to a patient.

3. Define a measurement interval.

4. Define the mean maximum baseline range of oxygen saturation ofhemoglobin over the measurement interval.

5. Define a desaturation event as at a specific uninterrupted decrementin saturation below the defined baseline range of oxygen saturation.

6. Measure the duration of the uninterrupted decline in saturation ofeach desaturation event.

7. Calculate the descending slope of each desaturation event.

8. Define a resaturation event as an uninterrupted rise in saturationwhich terminates the declining slope of the desaturation event.

9. Calculate the ascending slope of each resaturation event.

10. Define a phasic desaturation event as all coupled desaturation andresaturation events wherein the sum of the duration of the desaturationevent and the resaturation event is less than a specified value andwherein the descending slope falls within a finite range.

11. Calculate the descending to ascending saturation slope ratio of eachphasic desaturation event.

12. Define the number of probable apneic events within the measurementinterval by comparing said calculated slope with a value of slope whichis determined in advance to be indicative of sleep apnea, using any ofthe above techniques.

13. Identify each probable apneic event with an identity marker, oralternatively mark each event by its descending slope or by the sloperatio.

14. Treat the sleep apnea, either automatically, or manually, based on adiagnosis.

15. Repeat steps 1-14 to confirm the diagnosis and efficacy oftreatment.

The above system represents the general concepts of one embodiment ofthe present invention. Other comparisons which incorporate thedesaturation slope and the resaturation slope are also included withinthis teaching.

For example FIG. 5 shows how a comparison can use the calculation of thearea above each desaturation event compared to area above each coupledresaturation event. With this system, an x-axis is projected from apoint of initial desaturation. A second y-axis is projected upward fromthe initial point of rise of saturation which signifies the onset of aresaturation event. The areas above the sloping lines, defined as D andR in the above figure, are then compared in a similar manner to thatdescribed in the previous embodiment.

In addition, the specificity and sensitivity of oximetry with respect tothe diagnosis of sleep apnea is greatly enhanced by another embodimentof the invention which includes all of the multiple slope comparisons asdescribed above. In such a system, in combinations, the desaturationslope is compared to a desaturation slope which is consistent with adiagnosis of sleep apnea; second, the resaturation slope is comparedwith resaturation slopes known to be consistent with sleep apnea; third,desaturation slopes are compared with coupled resaturation slopes todefine a slope index which is known to be consistent with sleep apnea;fourth, desaturation slopes and resaturation slopes are compared withother such slopes within the same record to identify slope similarity ofthe desaturation slopes and slope similarity of the resaturation slopes,respectively; furthermore, the similarity of thedesaturation-resaturation slope index of the identified events can becompared; furthermore, as previously discussed; consecutive slopes canbe compared in relationship to the interval between desaturation eventsto determine whether a preceding desaturation event affects the slope ofa closely after following desaturation event, and; finally, the mean ofall desaturation slopes can be compared to the mean of all resaturationslopes to define an aggregate index.

In another embodiment, the present invention identifies a phasicdesaturation event to trigger storage or collection of at least oneadditional parameter of the patient. These additional parameters can be,for example, a recording of sound or video. When the microprocessoridentifies specific coupled desaturation-resaturation parameters whichare physiologically consistent with sleep apnea, the microprocessorinitiates the storage of selected data collected by at least oneadditional sensor.

Sound has been shown to be an important indicator of airway obstruction,however, many patients spend the majority of their night without majorobstructive apneas. Therefore, if the entire night of sound wererecorded, it would include a large amount of unnecessary soundrecording, for only a small amount of useful data surroundingobstructive apneas. In the preferred embodiment shown in FIG. 1, theadditional sensor includes a microphone 30 which can be integral with orcarried by the probe 13 of the pulse oximeter 12 or which can bepositioned in other regions near the patient during sleep. With thispreferred embodiment, the microphone 30 is connected to an audioprocessor 32 of any known type, such as a Sound Blaster(TM) 16-bitprocessor. The sound is recorded digitally as a function of time.Alternately, the sound may be Fast Fourier transformed (“FFT”), and thetransform information may be stored. Alternatively, other means of soundor other recording can be utilized.

Preferably, the sound is continuously recorded throughout the night andthe most recent recording always maintained in short-term memory. If,after a finite period of time (for example, 4 minutes), no coupleddesaturation-resaturation event occurs which is specific for sleepapnea, the oldest part of the recorded sound will be erased or otherwisenot marked for retrieval. If, however, a coupleddesaturation-resaturation event occurs which is consistent with sleepapnea, the identification of this event will trigger the marking andstorage of the collected sound data during an interval preceding,during, and immediately after the event.

In the preferred embodiment, the total sound interval retained for eachdesaturation event includes the interval of the coupleddesaturation-resaturation event, as well as one minute preceding and oneminute following each such event; although this recording time can befurther reduced for greater efficiency of memory utilization. In thisway, the entire night will be monitored by oxygen saturation while soundis stored, but the information can be rejected to save memory unless asleep apnea event is identified by pulse oximetry. If a sleep apneaevent is identified, this will trigger the long-term storage of soundinformation surrounding that event. In this way, the efficiency samplingof sound that can be greatly enhanced since only small portions of soundneed be collected in relationship to each apnea event.

Continuous recording of oxygen saturation and sound when indicated as afunction of time can be digitally stored on any commercially availibleremovable computer memory media, for example, a high-capacity floppydisc, or a removable Bernoulli disc, and then transported to a secondmicroprocessor for evaluation by the physician and for printing. Theentire record can be printed with a continuous graphical representationof oxygen saturation as a function of time. The sound can be graphicallyrepresented as a function of time by (for example, showing the volume asthe width of the line and the frequency as its position along they-axis). As discussed previously, such graphical representation ofoxygen saturation can include specific markers indicating coupleddesaturation and resaturation events which are physiologicallyconsistent with sleep apnea.

Preferably, staccato or interrupted low frequency sounds may also begraphically represented preceding an oxygen desaturation event.Subsequently, variable high frequency sounds of low volume may beidentified immediately preceding the recovery of oxygen saturation,indicating the presence of post-apnea hyperventilation. The physiciancan easily, therefore, determine whether these oxygen desaturationevents are due to obstructive sleep apnea by identifying the soundparameters with which these coupled desaturation-resaturation events aretemporally associated. Of course, all coupled desatruation events mightnot necessarily be associated with a typical sound pattern. However,throughout the night recording, patients with obstructive sleep apneawould be expected to have typical snoring sounds; whereas, patients withcentral sleep apnea from a periodic breathing or alterations inventilation-perfusion mismatch would not be expected to demonstrate suchsound parameters in relationship to such coupleddesaturation-resaturation events.

The system is further advantageous in that it allows the physician toefficiently focus on portions of the night which are of the greatestinterest. For example, the physician can specify a desaturation eventidentified by the microprocessor as an apnea, then either lookgraphically at the sound surrounding that event or, alternatively,listen to digitally-recorded sound which surrounds a specificdesaturation-resaturation event. It should also be clear that a videorecorder could be activated in a similar manner, along with a soundrecorder, to obtain critical bytes of a night's sleep for efficientevaluation. In this way, the diagnosis of airway obstruction can beconfirmed, along with the diagnosis of sleep apnea, by utilizing agreatly simplified and less expensive system than conventional homepolysomnography.

It is clear that, because of overlap with other disorders, the diagnosisof mild sleep apnea cannot be achieved by identifying a single coupleddesaturation-resaturation event even when the event and all theassociated slopes are physiologically consistent with sleep apnea. Forthis reason, the identification of a desaturation slope and aresaturation slope and a comparison of these slopes, even wherein allmeet the criteria for sleep apnea, can only be said to identify an eventthat is physiologically consistent with apnea from the perspective ofoxygen desaturation and resaturation waveform. It is the comparison ofmultiple desaturation events which is specific for sleep apnea as in thepresent invention.

Although, as per the previous embodiment, the analysis of slopeparameters when multiple events are identified and counted is specificwith respect to moderate to severe apnea, it is critical to achievespecificity for the large patient population that has only mild sleepapnea. Unfortunately, many disorders can produce oximetry waveformdeflections which are repetitive and/or cyclical and of equivalentmagnitude to those of mild sleep apnea.

Enhanced sensitivity must be achieved for patents with mild oximetrydeflections due to sleep apnea. In addition to providing enhancedsensitivity it is important for a system to make a rapid diagnosis ofthe presence of instant sleep apnea for CPAP titration. Themicroprocessor must make a definitive and reliable assessment of thepresence or absence of sleep apnea within a short interval to allow ahigher number of upward CPAP titrations throughout the night to assurethat the minimum opening therapeutic pressure has been identified aswill be discussed.

One preferred embodiment utilizes the continuous calculation andcomparison of saturation slopes to identify sleep apnea to therebyenhance sensitivity for mild apnea and achieve rapid diagnosis ofinstant sleep apnea. In this embodiment, as is conventional, oxygensaturation is measured as a function of time and each saturation datapoint is stored as a function of the sampling frequency. The presentinvention then utilizes each new data point with a preset number ofpreceding data points (for example, four data points wherein thesampling frequency is 20/min to derive a continuous instantaneous slope.The instantaneous slope is recorded as a function of time and can beplotted with saturation as a function of time on the same graph. In thispreferred embodiment, the instantaneous slope is calculated as the slopeof the line of best fit (as by conventional formulas) drawn through thespecified number of saturation points, such as 3-5 data points. As eachdata point is added, the new slope is recorded as a function of this newdata point with the first data point of the group deleted. This derivesa continuous moving waveform of the calculated slope of oxygensaturation/second, which is shown graphically in FIG. 6. In thepreferred embodiment, multiple consecutive slopes in the same directionare considered aggregate slopes and are averaged to produce a meannegative or positive aggregate slope. The continuous calculation andanalysis of this slope waveform provides an enhanced specificity in thediagnosis of sleep apnea with minimal compromise in sensitivity since itis not dependent on a specific threshold deflection for theidentification of apnea.

Since sampling frequency will determine the configuration of anyoximetry waveform. The greater the sampling frequency, the more reliablewill be the slopes in the presence of very mild sleep apnea. For mildsleep apnea, a sample recording interval of 3 seconds (wherein thelowest recorded saturation with this interval is recorded) is adequate,although a continuous sampling for each pulse is optimal for thisdiagnostic system.

In sleep apnea, oxygen desaturations generally occur within clusters.For the purpose of the present invention, a cluster is said to bepresent when at least three consecutive negative slopes interrupted bypositive slopes have occurred wherein the intervening interval betweeneach consecutive negative slope is less than two minutes. The presentinventor has discovered that the presence of a cluster of at least threenegative slopes meeting these criteria and wherein the consecutivenegative slopes are similar (for example, falling within a range of theinitial slope±60%) and wherein the negative-positive slope ratios arewithin 3.5-10.5 is clearly diagnostic of a sleep apnea cluster and canbe said to comprise a sleep apnea slope cluster complex, referred tohereinafter as a “slope cluster complex.” Such a slope cluster complex50 is graphically shown in FIG. 6.

In the presently preferred embodiment, the identification of slopecluster complexes is used to facilitate CPAP titration. With thissystem, the microprocessor can initiate nasal positive pressure at, forexample, a pressure of 4 cm of H₂O upon identification of a slopecomplex. As is known in the art, this pressure can be incremented froman initial 0 pressure up to 4 cm of H₂O pressure over a period of two tofive minutes or longer to minimize the potential for arousal withinitiation of therapy. Throughout this time, the pulse oximetry waveformis monitored for any evidence of further slope cluster complexes. If anadditional slope cluster complex occurs after the CPAP has reached 4 cmof pressure, the microprocessor again increases the CPAP level by anadditional 1 cm during the final negative slope of this new complex. Ifan additional after-following slope cluster complex again occurs themicroprocessor again increments, the nasal CPAP pressure by anadditional 1 cm during the final negative slope of this complex. Themicroprocessor will continue to monitor for further complexes andsimilarly, increment the nasal CPAP by 1 cm upon each recurrence up to apresent pressure limit of, for example, 15 cm. When no further suchcomplexes occur subsequent to an increment in CPAP, this level ismaintained for a sustained period, which should preferably be equal toor exceed 15 minutes. If any further slope cluster complexes occurwithin this interval, the microprocessor will increment CPAP by 1 cm ofH₂O pressure and this pressure will be maintained until no furthercomplexes are identified for 15 minutes. Once the baseline oxygensaturation has been without further slope cluster complexes for 15minutes, the CPAP is eliminated by the microprocessor. This can occurslowly over a period of, for example 2 minutes, to minimize thepotential for arousal to be induced by sudden reduction of nasal CPAP.The patient is then monitored again for evidence of recurrent slopecluster complexes, as previously described. If a slope cluster complexis again identified, the CPAP is incremented in a similar fashion tothat previously described; however, to allow more rapid titration, thestarting level of CPAP is set at a minimum of 2 cm H₂O below the finaltherapeutic level, which level was achieved during the precedingtitration. For example, if the preceding titration achieved atherapeutic CPAP of 10, the starting titration level for the titrationwould not be less than 8. (However, the CPAP unit can be ramped slowlyup to 8 over a period of 30 seconds, rather than suddenly initiatingthis pressure.) Again, incremental CPAP titration is utilized for eachconsecutive slope cluster complex, as for the initial titration, untilno further slope cluster complexes occur for the specified time intervalof 15 minutes. After the interval of 15 minutes without a slope clustercomplex has concluded, the CPAP will again be withdrawn, as previouslydescribed. The patient will be monitored and, if another slope clustercomplex occurs, a new titration will be initiated. In this way, as manyas 12 or more separate complete CPAP titrations can occur throughout thenight. Actually, however, less CPAP titrations generally will occur in amajority of patients since often there are no more than 4-5 separateclusters of desaturation events in any single night. To increase thenumber of titrations, CPAP may be withdrawn after a shorter specifiedinterval of absent slope cluster complexes, such as five minutes orupward titration may be more rapid, for example with each consecutivenegative slope within a slope cluster complex after an initial 3negative slopes have occurred. In this way, three or four CPAPtitrations may occur within a single 30 minute desaturation cluster.

The comparison of consecutive slopes within a cluster allows increasedspecificity with less loss of sensitivity by accepting the diagnosis ofsleep apnea without requiring a specific magnitude of desaturation fromthe baseline. This is particularly true when the slope cluster complexesare obliterated by initiation or incrementation of the CPAP levels.There is, of course, a time delay between the development of apnea andthe onset of oxygen desaturation identified by the pulse oximeter. Dueto this delay, it is not generally possible to arrest a specificnegative slope by the initiation or upward titration of nasal CPAPduring said negative slope unless the initiation occurs within a veryshort interval after the negative slope has started. Even when initiatedearly, substantial desaturation will continue, even if completeelimination of the obstruction immediately occurs upon initiation. Theinitiation of nasal CPAP during a slope cluster complex, therefore, mayeffectively treat and prevent the next negative slope, but unless theslope is quite prolonged the initiation or upward titration of CPAP maynot interrupt the negative slope which is already in progress since,indeed, the physiological mechanisms causing the negative slope may havealready have been completed. Anticipating this delay (which may be 20seconds or more) the CPAP can be initiated or titrated upwardimmediately upon identification of the third negative slope or at theend of the second negative slope. Arrest of the third negative slopeafter (he expected delay can provide diagnostic value.

The purpose of this repetitive cyclic titration is to identify abreakpoint range of CPAP which provides adequate pressure to break acycle of desaturations by preventing further apnea episodes. The effectis diagnostic and further identifies the level of CPAP required forlong-term therapy. The presence of even very small desaturations, whichoccur with slope cluster complexes and which are consistently eliminatedby a finite range of nasal CPAP pressures, is clearly diagnostic ofsleep apnea and specifies the level of CPAP that is required foreffective therapy. The recording of continuous CPAP pressure (as isknown in the art), may be simultaneously performed and, the recurrenttitration of the breakpoint can be identified by plotting the slopewaveform simultaneously with CPAP to verify that actual breakpoints areoccurring as a function of CPAP titration, rather than by chance. It isclear that with any single episode of titration, spontaneous cessationof sleep apnea cycles may occur at any time during the titration,providing an initial “breakpoint” which may actually not be truly afunction of adequate therapy. However, the consistent identification ofa single breakpoint range (for example, 8-10 cm) at which point, forexample, four separate slope cluster complexes were terminated andwherein no further slope cluster complexes occurred when this level wasmaintained would clearly identify adequate therapy and would identifythe lowest adequate therapeutic pressure.

Ideally, the entire titration process occurs over two nights. Theinitial titration process involves recurrent initiation and withdrawalof nasal CPAP, as previously described, over cycles of 15-30 minutesthroughout the night. The microprocessor identifies the breakpointpressure which is adequate to break all slope cluster complexesthroughout the entire night's study. This pressure level is designated“the therapeutic breakpoint CPAP” level and is recorded and stored forthe second night's study without requiring a second home visit or modemcontrol for adjustment of the CPAP. Upon initiation of the secondnight's study the microprocessor automatically ramps the CPAP unit up tothe therapeutic breakpoint CPAP value over a specified interval of, forexample 5-30 minutes. The patient is then maintained on this pressurelevel throughout the night to assure the pressure is adequate.

Importantly, for the sleep apnea diagnostic system to be utilized inclinical medicine, a hard copy must be produced so that the physiciancan overread the interpretation of the computer because many cardiac andpulmonary disorders, as well as artifact, can produce deflections withsimilar magnitude and configuration with that of mild sleep apnea. It iswell known that differentiation of mild sleep apnea by visualizing thepatterns of conventional oximetry waveform plots of oxygen saturationversus time is non-specific. The present invention describes a systemwhich derives and analyzes continuously the multiple slope patterns tomake a definitive diagnosis of sleep apnea quickly, however, for this tobe accepted by the medical community, the physician must have a new wayto interpret the oximetry tracing waveform and thereby overread theinterpretation of the computer. The present inventor has discovered thatthe slope cluster complexes are easily visualized even in the presenceof only mild apnea by graphically representing the continuous,instantaneous slope of oxygen saturation [as ΔSaturation (%)/ΔTime(seconds)] as a function of time, where slope is placed on the y-axisand time is placed on the x-axis. Utilizing this representation, theeffect of the limited magnitude of the deflection is greatly minimized,and the effect of the particular slope characteristics are maximizedgraphically and visually. The effect of visually and graphicallyrepresenting continuous slope as a function of time is shown in FIG. 6.This graphical representation allows the physician to overread theinterpretation of the computer by identifying visually the presence ofslope cluster complexes. As noted, this graph demonstrates the slope ofthe oxygen saturation as a function of time where:

slope=change in saturation ΔS (%)/ΔT (seconds),

and where time is in minutes.

The y-axis includes marked regions which identify slopes that arephysiologically consistent with sleep apnea. For example, with respectto negative slopes, the physiologically consistent region is marked as−0.3 to −1.1 and with respect to positive slopes, the physiologicallyconsistent region is marked as 2.5 to 8.3. As oxygen saturation datapoints are measured and stored, continuous slope calculations are made.Alternatively, consecutive negative or positive slopes may be defined asa single aggregate positive or negative slope and may be average for thepurposes of graphical representation and interpretation.

The previously-described apparatus is both a diagnostic tool for sleepapnea and a fixed therapeutic pressure identifier. Specifically, itidentifies the minimum fixed therapeutic pressure than can reliablyprevent substantially all future apnea episodes in a given patient. Thispressure is printed and identified as the minimum therapeutic breakpressure or optimal nasal CPAP pressure. In the preferred embodiment,the microprocessor may set the nasal CPAP pressure on the nasal CPAPunit for long term therapy so that this pressure is subsequentlymaintained for this patient without further adjustment by patient,physician, or home health personnel. This therapeutic pressure which hadbeen previously identified is, therefore, fixed and will be utilized,for example over the next 6-12 months, until a repeat study is performedat this pressure to confirm that further apneas have not redeveloped orthat a lower pressure might be therapeutic (such as after loss).

While this language herein refers to oxygen saturation, it should beunderstood that gas exchange parameters could be determined in waysother than those specifically disclosed herein, but are included withinthe scope of this teaching. For example, sequential and cyclictime-dependent storage of carbon dioxide in body compartments duringsleep apnea can be similarly used to diagnose sleep apnea using, forexample, the comparison of consecutive slopes of maximum exhaled pCO₂.Also, inspiration-triggered variable pressures, such as BIPAP, may alsobe titrated in a similar manner to that described herein for CPAP.

As previously described, obstructive sleep apnea produces a uniquepattern of oscillation of oxygen saturation which generally occurswithin clusters. The physiology of factors inducing upper airwayinstability is pivotal toward the derivation of the clusters which areidentified on the timed oxygen saturation wave form. Since sleep apneaitself can be considered a normal event, it is less important toidentify the presence of an episode of sleep apnea than to identify theof presence of upper airway instability. In the presence of upper airwayinstability clusters of coupled desaturation-resaturation events willoccur. The presence of such clusters meeting specific criteria is morethan diagnostic of sleep apnea. It is diagnostic of a severity of upperairway instability sufficient to produce an oscillatory aberration inthe normally homeostatic and stable interaction between respiratorydrive, upper airway tone, the arterial oxygen saturation.

Oscillatory Respiratory Drive During Sleep in the Presence of NormalUpper Airway Stability:

Decreased Drive-Hypoventilation-Increased Drive-Hyperventilation-MinimalDecreased Drive-Minimal Hypoventilation-Minimal Increased Drive-Normalventilation reestablished-(Drive Oscillation Extinguished)

Oscillatory Respiratory Drive during sleep in the presence ofsuperimposed upper airway instability (Note: Airway closure (Apnea)produces a block between a progressive increase in respiratory drive andits normal effect to increase arterial oxygenation. From the perspectiveof respiratory drive oscillation during sleep, each apnea functions likea potential energy store which is precipitously released upon thetermination of the apnea. The stored energy for increased drive is adirect function of the magnitude of oxygen desaturated hemoglobin. Whenthis energy is precipitously released, it function as a sudden new forceimpacting the oscillating system thereby preventing extinguishment. Thisrepetitive force loading thereby induces pronounced and sustainedoscillations which are manifested by the oximetry wave form as a clusterof coupled desaturation-resaturation events. For this reason a sustainedcluster, (wherein the precipitous release in transmission of heightenedrespiratory drive is manifested by a rapid increased in oxygensaturation after each fall) is diagnostic of sleep apnea and moreimportant diagnostic of upper airway instability during sleep (i.e.obstructive sleep apnea).

Therefore, in the presence of sufficient upper airway instability, fromthe perspective of gas exchange, and in particular as measured by thearterial pulse oximeter, the oscillatory sequence is substantiallyalways a cluster:

The above system may be modified in the presence of very severe upperairway instability. In this condition hypoventilation may not be anecessary antecedent to apnea and indeed upper airway patency may becomecompletely arousal dependent in certain sleep stages (e.g. during REM)or with a particular body or head position. In this highly unstablesystem the oscillatory frequency is a function of the arousal threshold(which determines the length of the apnea) and the duration of eacharousal (which determines the time interval between recovery and thenext desaturation). These are variable as a function, for example ofsleep stage, and the magnitude of sleep deprivation. For this reason, inthe presence of a potentially highly unstable airway the intraclusterdesaturation/resaturation frequency may be variable. Since, in theabsence of arousal, the highly unstable airway will promptly occlude,rather than being driven by more complex oscillation of respiratorydrive as defined above, such a highly unstable system is defined by amore simple on and off alternation of airway patency (as a function ofthe presence or absence of the arousal state). In the presence of ahighly unstable upper airway, the oximetry wave form will appear as if aoxygen flow switch was turned off for a specific time interval to asystem having a relatively finite oxygen runoff and then turned back onprecipitously releasing a high rate of flow rate of oxygen back into thesystem only to again be promptly turned off again. Although the nadir ofthis wave form may appear similar to that associated with a lessunstable upper airway, the inter-event peaks often present a sharperangle reflecting the substantially absolute “on or off” mechanism of gasflow to the patient especially if each arousal is brief.

Since the variations in arterial oxygenation which comprise a clusterare derived from an oscillating respiratory drive system, within eachcluster, the desaturation/resaturation events occur at relativelyregular intervals as a function of the temporal relationship betweenovershoot ventilation, hypoventilation, and upper airway collapse or asa function of the existence of a threshold range of oxygen desaturationwhich will “turn on” arousal. Within a cluster, eachdesaturation/resaturation event has a similar shape. In some cases, thedesaturation/resaturation events within a given cluster start out withfairly small desaturation magnitude (change from baseline), increase inabsolute magnitude and then decrease in magnitude before end of thecluster. In other words, the magnitude of the desaturation/resaturationevents waxes and wanes once during a cluster. The clusters, themselves,typically do not occur at regular intervals and the clusters also do nothave the same time length. The signal interval between clustersgenerally has no particular interesting characteristics and might bethought of as being a signal with some noise and/or artifact. Patientswho do not have OSA, on the other hand, may havedesaturation/resaturation events but they do not have the sameperiodicity that is apparent with OSA patients and thesedesaturation/resaturation events often do not have any particularcharacteristic shape.

There are several methods for transforming a time domain signal, i.e. asignal which varies in amplitude as a function of time, into thefrequency domain to determine its frequency content. Fast Fouriertransform (FFT) analysis, a well-understood and well-utilized analysistool, is one such method. Many efficient hardware and softwarealgorithms have been developed and implemented which can perform FFTanalysis on a time-domain signal segment. An FFT algorithm approximatesa time domain signal segment with a series of sinusoidal functions. Eachfunction in the series has a certain amplitude, frequency and phase. Ifthese functions are summed in the series point-for-point over time, theresultant time-varying signal will resemble the original time domainsignal segment analyzed. The more sinusoidal functions used toapproximate the signal, the better the resemblance. The time domainsignal is represented in the frequency domain by plotting the amplitudeversus frequency of the sinusoids in the series. This gives avisualization of the frequency content or frequency spectrum of theoriginal signal. It allows for visually identifying the dominantfrequencies that make up the original time domain signal. Another commonvisualization of the original time-domain signal segment is the powerspectrum which is constructed by plotting the square of the amplitudesof the sinusoids resulting from FFT analysis versus the frequency of thesinusoids in the series.

Frequency domain analysis of the SpO₂ signal, as performed with an FFTalgorithm, reveals several qualitative and quantitative characteristicswhich provide a unique signature for OSA. In the frequency domain,signals recorded from OSA patients will have at least one and possiblytwo specific dominant (high amplitude) frequency intervals in thefrequency spectrum, depending on the particular characteristics of thesignal and time length of the signal segment analyzed. The primaryfrequency interval will result from the individualdesaturation/resaturation events. This interval will have a fairly sharppeak because the events are somewhat sinusoidal in shape and are fairlyequally spaced in time. Only a few frequency components will be neededto reconstruct this part of the signal. The frequency of the peak power(FP) will range between approximately 0.01 Hz to 0.08 Hz. The seconddominant frequency interval will result from the fact that thedesaturation/resaturation events often occur in clusters. If the signalsegment being analyzed includes several clusters, the result will reveala second dominant frequency interval which will correspond to theperiodicity of the clusters in time. It has been shown that thedesaturation and resaturation slopes along with the time betweendesaturation/resaturation events, in the time domain, fall within anarrow range for OSA patients. Therefore, the inter-patient variabilityof the first described dominant frequency will be relatively small. Ithas been found that when definable clusters appear in the SpO₂ signal ofOSA patients, the clusters do not often occur at regular intervals andare not of the same time length. At times the entire nights tracing iscomprised of a single continues cluster. Therefore, the second describedfrequency interval, if observed, will display a large inter-patientvariability. Signal intervals between clusters will generally have aflat spectrum because these signal intervals are composed mainly ofbroad-banded noise and random artifact or defections derived fromphysiologic mechanism which are not inherently oscillatory. The majorityof SpO₂ signals from patients who do not have OSA have no particularoutstanding features but appear rather as random noise, non periodicdeflections and/or artifact, the frequency spectrum of these signalswill generally be broadbanded, flat, and may lack any dominanthigh-amplitude frequency intervals. There are several quantitativeparameters that can be extracted from the power spectrum of the SpO₂signal which provide a signature for OSA. Of particular note is theratio peak power (PP)/bandwidth (BW). Peak power is defined, for a giventime-domain signal segment, as the highest power in the power spectrum.The BW, of the same signal segment, is defined as the difference betweenthe highest frequency below which resides a given percentage of thetotal power minus the lowest frequency above which resides the samepercentage of total power. FIGS. 7-11 demonstrate the utility of thisparameter in identifying OSA. FIG. 1 shows a segment of the time-domainSpO₂ signal recorded from a patient with OSA and illustrates thecharacteristic repetitive oxygen desaturation/resaturation events seenwith this disease. FIG. 8 shows the power spectrum of this signalsegment, which resulted from FFT analysis following appropriatefiltering and windowing. Because the time-domain signal is somewhatsinusoidal in shape and periodic with near constant period, the powerspectrum shows a narrow frequency interval around the PP which islocated in the spectrum at the frequency of thedesaturation/resaturation repetition rate. The narrow BW enhances thehigh PP to yield a large PP/BW ratio value. In contrast, FIG. 9 shows atime-domain signal segment from a OSA disease-free patient. In thispatient, the saturation tends to rise and fall during the segment butshow less periodicity and more noise. The resulting power spectrum (FIG.4) shows a broadband signal with peaks that have much less power than inFIG. 8. As a result, the PP/BW ratio is several orders of magnitudesmaller than in the OSA patient.

Another important frequency parameter which can be used, in combinationwith other parameters, to identify patients with OSA is the peak powerfrequency (FP), defined as the frequency containing the peak power, PP.It has been shown that the rate at which desaturation/resaturationevents occur falls within a specific range and is mostly independent ofa patient's physiology and habitus. Therefore, the FP for OSA patientsfall within a definable range of frequencies. This range isapproximately 0.01 Hz to 0.08 Hz. The value of the PP/BW ratio and FP asdiagnostic parameters for OSA is illustrated in FIG. 11. The median (±25percentile) of PP/BW, in the range 0.01 Hz to 0.08 Hz, is plotted forboth disease-free and OSA patients. The PP/BW is significantly greaterfor the OSA patients than for the disease-free patients. This occursbecause the PP is higher and the associated BW is narrower for the OSApatients than for the disease-free patients. FIG. 11a illustratesanother use of the PP/BW ratio for diagnosing. This data was generatedin a similar manner as in FIG. 11 except that the total of all PP/BWvalues over the sleep session for each patient, rather than the average,was used as the diagnostic parameter. All other aspects of the analysiswere the same as in FIG. 11. This summation parameter represents thearea under the PP/BW vs time curve over the sleep session. As in FIG.11, the median (+25 percentile) of the PP/BW summation values wasplotted for both disease and OSA patients. The PP/BW summation issignificantly greater for the OSA patients than for the disease-freepatients. In this patient sample, the PP/BW summation achieved an 89%sensitivity with an 82% specificity as a diagnostic parameter for OSA,when the threshold for diagnosis was set at 175%%/Hz.

Since OSA patients often experience multiple clusters ofdesaturation/resaturation events during a sleep session, the PP andPP/BW during these clusters is virtually always larger than duringperiods of normal, non-apneic sleep. For this reason, a patient may bediagnosed as having OSA if, for instance, either PP or PP/BW or both PPand PP/BW exceed a specific threshold—for example, PP>5%%/Hz and/orPP/BW>175%%/Hz—a specific number of times during a sleep session or fora specific cumulative duration (e.g. 3 times or for a total cumulativeduration of 15 minutes. These thresholds would be exceeded during a timeinterval when the patient is experiencing a cluster ofdesaturation/resaturation events.

A block diagram for one presently preferred embodiment for screening forpatients with OSA utilizing a frequency-domain algorithm to achieve ahigh sensitivity is shown in FIG. 12. The consecutive epochs of thenocturnal SpO₂ wave form are transformed into the frequency domain viaFFT analysis 1. The time-length of each epoch tp is identical and in therange 5-10 min. The sampling frequency fs of the wave form is >1 Hz. Thepower spectrum of each resulting frequency spectrum is then evaluated bysquaring the amplitude of each component of each frequency spectrum 2.The peak power PP and peak power frequency FP, in the frequency range0.01-0.08 Hz, and the power spectrum bandwidth BW are then extractedfrom each power spectrum 3. The peak power/bandwidth ratio PP/BW is thenevaluated for each power spectrum 4 and the median PP/BW is calculatedfor the entire sleep session 5. This median PP/BW is compared with theOSA threshold PP/BW 6 and if it is greater than the threshold thenprobability that OSA is present is considered very high 7 (positivescreening test). Otherwise, clinically significant OSA is virtuallyruled out 8.

A block diagram for the presently preferred embodiment for diagnosingpatients with OSA is shown in FIG. 13. This embodiment incorporates acombined time and frequency-domain algorithm to achieve both highsensitivity and specificity for clinically significant OSA. The combinedtime-frequency algorithm has the advantage of providing differentiationbetween other sleep disordered breathing disorders associated withenhanced respiratory drive oscillations (e.g. Cheyne-Stokes Respiration)which may exhibit frequency parameters which are similar to obstructivesleep apnea and therefore less subject to differentiation when afrequency algorithm is applied in isolation. The nocturnal SpO₂ waveform is first searched to identify all desaturation/resaturation events1. A valid desaturation/resaturation event is defined as consisting of adesaturation Sd of >=2% (>=3% can be used also) from baseline with adesaturation rate Md in the range 0.2-1.2%/s. This is followed by aresaturation back to baseline with a resaturation rate Mr such thatMd<=Mr. Because a desaturation/resaturation event has a characteristicshape, a range of spatial image templates can be constructed andcross-correlated along the wave form in order to find actions of thewave form which are highly correlated with the template either to locatea desaturation/resaturation event or to identify the cluster pattern.All of the desaturation resaturation events are then grouped intoclusters 2. A cluster is defined as a group of at least three coupleddesaturation/resaturation events where the time between each consecutivecoupled event is <30 s, the dominant event frequency falls in the range0.01-0.08 Hz, Md<Mr when Sd>=4%. Then, OSA is diagnosed 5 if at leastthree clusters occurred during the sleep session 3 or if at least oneclustered had at least nine coupled desaturation/resaturation events 4.Otherwise, clinically significant OSA is virtually ruled out 6. Inconjunction with that previously discussed the present inventor hasdiscovered an apparatus and method of waveform analysis for thediagnosis of sleep apnea which can be used to evaluate a range ofwaveform including oximetry and exhaled carbon dioxide. For reasonsnoted supra, with respect to the diagnosis of sleep apnea, the oximetrywaveform (with or without combination with digital sound) is preferred.The presently preferred embodiment includes a computer program forrecognizing the basic cluster pattern (which pattern has been previouslydescribed) through the use of object designation to identify eachcomponent of the pattern and then though pattern reconstruction withinthe program. The present inventor has discovered that the identificationof this pattern, even in isolation, wherein sequential oscillations aresufficiently perpetuated to produce recognizable clusters is indicativeof significant sleep apnea since the recognizable cluster is in fact asentinel event which indicates the presence of much more frequent, lesssevere events which do not result in sufficient desaturation forrecognition. Specificity is derived by requiring a specific pattern orsequence of events meeting predetermined criteria or characteristicswithin this sentinel cluster, which pattern or events are the product ofa combination of ventilation control instability and upper airwayinstability. Whereas, sensitivity is derived by eliminating therequirements of a certain number of events per hour such that therecognition of a specific sentinel cluster pattern is sufficient to makethe diagnosis. One presently preferred method includes theidentification of a specific waveform objects and the comparison of thecharacteristics (for example the slope) of these objects in a mannersimilar to that previously discussed. The waveform is segmented into aspecific sequential basic object (the saturation Dipole) having thecharacteristics of polarity, duration, and slope and from which alladditional objects are derived. The system includes a microprocessorwhich divides the basic timed oxygen saturation waveform into said slopedefined dipoles and then further classifies sequential components of thewaveform as slope and interval derived basic objects for microprocessoranalysis. The microprocessor then performs an analysis of the objects tofurther derive more complex objects of the oxygen saturation wave formcalled bipolar oscillations and slope clusters (which comprise multiplebipolar oscillations). These complex objects are built by themicroprocessor by first identifying the basic object and then by addingeach new sequential basic object to the preceding objects when saididentification indicates that an appropriate basic object has occurredwithin an acceptable order and within an acceptable time interval. Thebuilding of complex objects from the repetitive derivation of basicobjects along a timed waveform exploits the unique self perpetuatingphysiological events which occur during obstructive sleep apnea andwhich produces clustering of similar oxygen saturation slopes andintervals within predictable intervals of each other and in apredictable order sequence.

One embodiment of the system is configured and operates as follows:

1. A microprocessor based sleep apnea diagnostic system beingconstructed to:

2. Measure oxygen saturation.

3. Store oxygen saturation as a function of time to derive a saturationto time waveform.

4. Identification of two consecutive data points along said waveformsaid data points comprising, for the purpose of microprocessor analysis,a basic object specified as a saturation dipole or “Dipole” and then to;

Define the Dipole as positive when the second data point is higher thenthe first point.

Define the Dipole as negative when the second data point is lower thenthe first point.

Define the Dipole as neutral when the second data point is the same asthe first point.

(A threshold magnitude of increase or decrease may be used to definewhen the second data point is specified as higher or lower than thefirst data point.)

5. Calculate the slope of the Dipole as, for example, (Y₁-Y₂)/X

Where;

Y1=first saturation in percent

Y2=second saturation in percent

X=the measurement interval of the Dipole in seconds

6. Identify the next consecutive data point along said waveform saidnext data point with the immediately preceding data point (i.e. thesecond data point of the first dipole) comprising the next Dipole andthen;

a. Define the next Dipole as positive when the next data point is higherthen said preceding data point.

b. Define the next Dipole as negative when the second data point islower then said preceding data point.

c. Define the next Dipole as neutral when the second data point is thesame as said preceding data point.

7. Calculate the slope of the next Dipole.

8. Repeat steps 6 and 7 along said entire waveform (for examplespecifying each consecutive Dipole as Dipole_((1+Z)) where Z is equal tothe number of preceding measured data points.)

9. Compare the polarity of each consecutive Dipole.

10. Define an Aggregate Dipole as two or more consecutive Dipoles of thesame polarity

11. Calculate the slopes of all Aggregate Dipoles.

12. Define an Event as an Aggregate Dipole which includes the maximumnumber of Dipoles occurring consecutively with the same polarity. (Athreshold number of dipoles or a threshold duration may be used tofurther define an event)

a. Define the Event as a “Resaturation” when the slope of said Event ispositive and the duration of said Event is greater than 5 seconds andless than 60 seconds.

b. Define the Event as a “Desaturation” when the when the slope of saidEvent is negative and the duration of said Event is greater than 9seconds and less than 160 seconds.

c. Define the Event as a “Plateau” when the slope of said Event is zero.(A threshold magnitude of increase or decrease in slope (e.g. greaterthan 0.2%/sec. or less than—0.2%/sec.) may be used to define when saidslope is above or below a plateau range)

13. Calculate the mean slopes of all Events.

14. Define a negative oscillation as a Desaturation followed by aResaturation within a specific interval of less than 15 seconds andcalculate the duration of said oscillation defined by the beginning ofsaid Desaturation to the end of said Resaturation.

15. Define a positive oscillation as a Resaturation followed by aDesaturation within a specific interval of less than 60 seconds andcalculate the duration of said oscillation defined by the beginning ofsaid Resaturation to the end of said Desaturation.

16. Define a Bipolar Oscillation as a Negative Oscillation followed by aPositive Oscillation wherein the desaturation of said PositiveOscillation occurs within 60 seconds of said Negative Oscillation.

17. Define a Coupled Oscillation as two consecutive NegativeOscillations wherein the desaturation of the second Negative Oscillationoccurs within 60 seconds of the end of the Resaturation of the firstNegative Oscillation.

18. Compare the slopes of each consecutive Desaturation with a firstrange of slopes consistent with sleep apnea with a range of slopesconsistent with sleep apnea.

19. Compare the slopes of each consecutive Resaturation with a secondrange of slopes consistent with sleep apnea.

20. Define a cluster as at least 3 consecutive coupled oscillationswherein at least 75% of said slopes fall within said specific respectiveranges.

21. Diagnose Sleep Apnea if a cluster is identified having at least 4said consecutive coupled oscillations or if at least two clusters areidentified.

FIG. 14 illustrates mapping of an object oriented analysis system usinga sequentially building process through the identification (bycomparison with predetermined ranges of slopes and intervals) of eachbasic object and then by the addition of this basic object to thepreceding object if said identified basic object occurs within apredetermined interval and in a predetermined order of sequence. In thisone preferred embodiment the property types of specific components ofthe oxygen saturation/time waveform are defined such that the specificcomponents are treated each as an individual object for both the purposeof identification and comparison. FIGS. 15a and 15 b provides anillustrative procedural embodiment written in visual basic version 5which iterates through a set of consecutive saturation points andidentifies and creates objects including dipoles, events, oscillationsand coupled oscillations as a function of the specific parametersdiscussed supra. As illustrated in FIG. 14, each object is defined by aone or more parameters which may have a specific range. For example, thefirst apnea related desaturation of the first cluster (which isdesignated DESAT 1.1) must fall within a specific range of slopes andduration to receive that designation, (these ranges have been discussedearlier) and can be defined as having a beginning and an end (forexample as a function a maximum duration and of said slope range). Inanother example, the first nadir interval (NI 1.1)—defined as theinterval from the end of DESAT 1.1 and the onset of the first recovery(REC 1.1), is defined as falling within a specific maximum duration(such as within 15 seconds). While the first peak interval (PI 1.1)—theinterval between the end of REC 1.1 and the beginning of the seconddesaturation (DESAT 2.1) can also be defined as falling within aspecific maximum (such as within 90 seconds). The basic objects mustoccur within a limited falling within a specific maximum duration (suchas within 15 seconds). While the first peak interval (PI 1.1)—theinterval between the end of REC 1.1 and the beginning of the seconddesaturation (DESAT 2.1) can also be defined as falling within aspecific maximum (such as within 90 seconds). The basic objects mustoccur within a limited time interval of each other and must occur in aspecific order to function as the appropriate basic objects (buildingblocks) for the derivation of more complex objects.

In this analytic embodiment each frequency measurement derived from eachobject (which can be designated as ƒ Object x.x) is said to represent aparallel object. (e g ƒ NOS 1.1 is an object having a parallelrelationship to the object NOS 1.1 and can for example be compared andshould be similar to object ƒ NOS 1.2). In the analysis, the complexobjects are built from the basic objects and entire waveform may bescanned for each basic and complex object and then the objects compared.In this way both the identification of each object and the comparison ofeach object with other objects contributes to increased specificity inthe diagnosis of sleep apnea.

When a microphone and audioprocessor are included, as shown in FIG. 1,each dipole can be coupled for analysis and comparison with otherdipoles to the digital sound occurring within the dipole time interval.In this situation the each specific dipole object or a each more complexobject (such as a negative oscillation) will have the additionalproperty attributes of digital sound such as the presence of sound, theabsence of sound, frequency, volume, and quality. Both absolute(presence or absence of sound over background) timing and frequencytiming is an important feature which can be linked to more complex timeinterval of each other and must occur in a specific order to function asthe appropriate basic objects (building blocks) for the derivation ofmore complex objects.

In this analytic embodiment each frequency measurement derived from eachobject (which can be designated as ƒ Object x.x ) is said to represent aparallel object. (e.g. ƒ NOS 1.1 is an object having a parallelrelationship to the object NOS 1.1 and can for example be compared andshould be similar to object ƒ NOS 1.2). In the analysis, the complexobjects are built from the basic objects and entire waveform may bescanned for each basic and complex object and then the objects compared.In this way both the identification of each object and the comparison ofeach object with other objects contributes to increased specificity inthe diagnosis of sleep apnea.

When a microphone and audioprocessor are included, as shown in FIG. 1,each dipole can be coupled for analysis and comparison with otherdipoles to the digital sound occurring within the dipole time interval.In this situation the each specific dipole object or a each more complexobject (such as a negative oscillation) will have the additionalproperty attributes of digital sound such as the presence of sound, theabsence of sound, frequency, volume, and quality. Both absolute(presence or absence of sound over background) timing and frequencytiming is an important feature which can be linked to more complexobjects of longer duration. As previously discussed, the frequency ofsound is different with different portions of the sleep apnea cycle.After adjusting for the delay (as will be discussed), the first portionof a positive oscillation is associated with the high frequency soundsof recovery related hyperventilation through a widely open airway, thesecond portion (including a portion of the peak interval) is associatedwith the low frequency sounds of vibrations generated by partiallyoccluded upper airway, and the terminal portion is associated with theabsolute absence of sound (over background noise). Each timed saturationevent object can be evaluated with its coupled sound (which sound can beinitially transformed into the frequency domain).

A delay (as is known in the art) is associated with the response of thepulse oximeter and since this delay is in part a function of the bodypart chosen for probe placement (e.g. the delay is longer for the toethan the finger). This delay can be factored into the matching ofspecific timed oxygen saturation objects so that each object is matchedmore accurately with the sound occurring in association with thespecific event or object. For example a patient can demonstrate a“desaturation delay” with a Ohmeda 3760 oximeter of 75 seconds from theonset of apnea (cessation of sound) to the beginning of the desaturationevent whereas the “resaturation delay” from onset of airway opening andhyperventilation (initial high frequency sound) to the onset of theresaturation event in the same patient can be 50 seconds. The differencebetween these two delays is a function of the presence of oxygen storagein the lungs and the venous blood as previously discussed and the lossof difference between the desaturation delay and the resaturation delayis evidence of low oxygen stores at apnea onset. For example thedesaturation delay interval may fall during a cluster of severe apneas.

In this way the time of onset of apnea can be determined by theidentification of the point at which cessation of low frequency soundoccurred wherein said cessation was followed within an acceptabledesaturation delay interval (for example between 40-90 seconds) by adesaturation event meeting the characteristics of a sleep apnea relateddesaturation. The time point of recovery can be identified by the pointof onset of high frequency sound wherein said point follows saidcessation of low frequency sound within a finite period (for exampleless than 3.5 minutes) and wherein said point of onset is followedwithin an acceptable resaturation delay interval (for example between30-60 seconds) by a saturation event meeting the characteristics of asleep apnea related resaturation. Apnea duration by sound index may thenbe calculated as the time point of low frequency cessation less the timepoint of high frequency onset. As described above the combination ofsound characteristics coupled with specific identified, slope defined,objects along the oxygen saturation waveform provides enhanced sleepapnea diagnostic capability with only a single connection to the patientduring sleep.

In another preferred embodiment the identification of desaturationregularity and frequency is used to identify the presence of a cluster.The waveform is scanned by a method for identifying a desaturation event(as by one of the previously described methods). The presence of acluster of desaturations is identified wherein the interval betweenconsecutive desaturations is regular and falls within a maximum range(such as 210 seconds). The mean interval between desaturations and thedesaturation event frequency for each clustering of desaturations isidentified. The presence of irregular occurring clusters of regulardesaturations has similar diagnostic relevance to the identification ofirregular clusters of regular oscillations but with less specificity. Inone preferred embodiment, the sleep diagnostic system described abovecan be designed so that it can be coupled with a conventional airflowmeasurement device (such as a spirometer) to enhance the value to theprimary care physician or lung specialist in sleep diagnosis. It isuseful to know the minute ventilation (liters of air inhaled and exhaledby the lungs per minute) which relates to a given patient's arterialoxygen saturation. In addition, and perhaps more importantly, it isuseful to know the magnitude and rate of change in oxygen saturationinduced by a given change in minute ventilation or, alternatively, themagnitude of change in minute ventilation required to achieve a givenchange in oxygen saturation. Furthermore, a patients tolerance duringthe awake state for a given fall in level of ventilation or oxygensaturation can provide evidence in support of the presence of sleepdisordered breathing. For these purposes the sleep apnea diagnosticsystem described above can be enhanced for use in the physicians officeby providing with the microprocessor a connection to a the output of aconventional spirometer having a pnuemotach for measuring minuteventilation. The microprocessor includes an algorithm to integrate theoximetry output with the spirometry output to generate the timed oxygensaturation waveform coupled to a timed minute ventilation waveform forcomparisons between the absolute values and slopes of the oxygensaturation with the minute ventilation.

Patients with hypoventilation syndromes often have modestly increasedarterial partial pressures of carbon dioxide during the day. In suchpatients arterial oxygen saturation may be normal (e.g. 91-93%) butbecause the carbon dioxide related drive to breathe is reduced in thesepatients it is often easy for these patients to volitionallyhypoventilate while awake. Since these patients often have normal deadspace ventilation and live near the steeper portion of the oxyhemoglobindissociation curve, a reduction in minute ventilation by 50% results ina characteristic fall in oxygen saturation which is not easily achievedby a patient with a normal drive to ventilate. Furthermore patients withintrinsic lung disease may have high dead space ventilation such thatmarked changes in ventilation have little effect on oxygen saturation.Using the combined device, the a timed fall can be induced in severalways:

The patient is allowed to lie in a recumbent position for 5 minutes. Anoximeter is attached to the patient, a pneumotachometer is placed in thepatients mouth (a nose clip is applied) and the patient is told tobreathe normally through the pneumotachometer until a stable baselineminute ventilation is identified (this can be automatically identifiedby the microprocessor). Once this has been completed one of thefollowing maneuvers can be utilized.

1. The patient is instructed to hyperventilate (as to a certain minuteventilation threshold which may for example be 5 times the baselinelevel) for 20-30 seconds and then the patient is told to rest (with thepnuemotach in place). Patients with a reduced drive to ventilate willoften demonstrate marked overshoot in the post hyperventilatory periodwith a marked fall in minute ventilation and a brisk fall in oxygensaturation to levels below the pre hyperventilation baseline whereasnormal patients generally do not demonstrate significant overshoot inthe awake state. A coupled, timed arterial oxygen saturation and minuteventilation waveform is recorded and plotted for the entire maneuver andthe slope of the post hyperventilatory fall in saturation is calculated.A fall of more than 3% below prehyperventilation baseline is indicativeof reduced ventilatory drive and suggestive of a high risk of sleepdisordered breathing.

2. The patient is instructed to slow breathing down to a threshold level(e.g. 50% of the baseline level). Patients with reduced drive willtolerate lower minute ventilation during wakefulness thereby resultingin oxygen desaturation generally below 90%. A fall in oxygen saturationto levels below 90% without an extreme sense of shortness of breath issuggestive of reduced ventilatory drive and suggestive of a high risk ofsleep disordered breathing.

3. The patient is instructed to hyperventilate (as to a certain minuteventilation threshold which may for example be 5 times the baselinelevel) for 5 seconds and then the patient is told to rest (with thepnuemotach in place) for 15-25 seconds and then the cycle is repeated upto 5 times. In a manner similar to the single prolonged hyperventilationdescribed above, patients with a reduced drive to ventilate will oftendemonstrate marked overshoot in each post hyperventilatory period with amarked fall in minute ventilation and a brisk fall in oxygen saturationto levels below the pre hyperventilation baseline (greater than 3% belowbaseline) whereas normal patients generally do not demonstratesignificant overshoot in the awake state. A coupled, timed arterialoxygen saturation and minute ventilation waveform is recorded andplotted for the entire maneuver and the slope of the posthyperventilatory fall in saturation is calculated. An abnormal arterialsaturation curve indicative of reduced ventilatory drive willdemonstrate a cyclic pattern with slopes similar in desaturation andresaturation to that described above in sleep apnea, this is suggestiveof a high risk of sleep disordered breathing.

4. The patient is instructed to exhale completely and hold his or herbreath as long as possible, then take 4 deep breaths quickly and repeatthe exhale—breath holding—hyperventilation cycle for 4-5 times. Apattern of cyclic desaturation similar to that occurring in sleep apneacan occur in normal individuals with a highly motivated breath-holderbut rapid desaturation slopes and tolerance to marked falls in oxygensaturation are suggestive of a high risk of sleep disordered breathing.Patients with an early and brisk fall in oxygen saturation with breathholding have either a low residual volume and/or a low mixed venousoxygen saturation. Especially in the non-obese, this simple,inexpensive, and noninvasive maneuver can provide a clue to the presenceof cardiac disease especially if a biphasic (initial rapid desaturationslope and later slower desaturation slope) fall in oxygen saturation isnoted (as discussed earlier for sleep apnea).

The following are examples of clinically useful indices which can becalculated by the described embodiment (the corresponding time intervalfor each of these indices is adjusted for the delay in oxygen uptake andtransmission into oxygen saturation data by the pulse oximeter as isknown in the art):

1. The saturation to ventilation index

(SaO2_(ta)−84)/Ve_(t)

Where;

Vet=the average minute ventilation during a time interval and,

Sao2t=the average arterial saturation during the corresponding timeinterval

The higher this index the greater the probability of a hypoventilationdisorder and attendant sleep disordered breathing (As shown, this indexis only for saturations of 85 or above)

2. The delta saturation to delta ventilation index

d SaO2_(ta)/d Ve_(t) (change in % saturation/change in minuteventilation)

Where;

Ve_(t)=the average change in minute ventilation during a time interval(either increase or decrease) and,

SaO2 _(ta)=the average change in arterial saturation during thecorresponding time interval

Patients with intrinsic lung disease have a lower index than normal.This index helps differentiate whether a low baseline saturation is dueto hypoventilation or intrinsic lung disease.

Although the presently preferred embodiments of this invention have beendescribed, it will be obvious to those skilled in the art that variouschanges and modifications may be made, in particular for themicroprocessor based recognition of the cluster, without departing fromthe invention. Therefore the claims are intended to include all suchchanges and modifications which may be made therein without departingfrom the invention. Therefore the claims are intended to include allsuch changes and modifications that fall within the true spirit andscope of the invention.

1. The saturation to ventilation index

(SaO2_(ta)−84)/Ve_(t)

Where;

Vet=the average minute ventilation during a time interval and,

Sao2t=the average arterial saturation during the corresponding timeinterval

The higher this index the greater the probability of a hypoventilationdisorder and attendant sleep disordered breathing (As shown, this indexis only for saturations of 85 or above)

2. The delta saturation to delta ventilation index

d SaO2_(ta)/d Ve_(t) (change in % saturation/change in minuteventilation)

Where;

Ve_(t)=the average change in minute ventilation during a time interval(either increase or decrease) and,

SaO2 _(ta)=the average change in arterial saturation during thecorresponding time interval

Patients with intrinsic lung disease have a lower index than normal.This index helps differentiate whether a low baseline saturation is dueto hypoventilation or intrinsic lung disease.

FIG. 16 illustrates a preferred compact single unit configuration whichthe inventor calls “portable flowoximetery” which allows portabledetermination of the above parameters. This configuration is achievedby:

1. Combining an oximeter and spirometer into a single compact casingwhich can be easily carried to the bedside.

2. Attaching a pulse oximeter probe and a flow sensor to the integratedpulse oximeter.

3. Using the microprocessor, integrating the timed oximetry signal andtimed exhaled (or inhaled) gas flow (an adjustment may be made for theoximetry signal delay as described supra.)

4. providing output of oxygen saturation indexed for the timed oraveraged exhaled volume.

FIG. 17 illustrates an embodiment for office use. The configeration andoperation is acheived by:

1. providing an oximetry signal input receiver as part of an officespirometer and then,

2. using the microprossor, integrating the timed oximetry and timed flow(an adjustment may be made for the oximetry signal delay as describedsupra.)

3. providing output of oxygen saturation indexed for the timed oraveraged exhaled volume.

FIG. 18 illustrates an embodiment for use with existing oximeters andspirometers having output jacks which can be accessed for connection toa central microprocessor. The configuration and operation is achievedby:

1. connecting the output of an oximeter and the output of a spirometerto a microprocessor,

2. Using the microprocessor, integrating the timed oximetry flow (anadjustment may be made for the oximetry signal delay as describedsupra.)

3. Providing output of oxygen saturation indexed for the timed oraveraged exhaled volume.

The previously described sleep apnea diagnostic system of the presentinventor can be used to determine the severity of sleep apnea. Studieshave demonstrated that the standard “apnea hypopnea index” which iscalculated by counting the number of apneas and hypopneas and dividingby the number of hours of sleep is a poor indicator of disease severity.There has long been a critical need for a new method to assess severityand indeed ongoing studies sponsored by the National Institute of Healthare attempting to identify the validity of the apnea hypopnea index andto identify a valid signal of disease severity. As discussed supra thecluster characteristics can be used to define severity. The presentinventor has discovered a system and method, which can be used toenhance the determination of disease severity. In the presentlypreferred embodiment, this system and method determines a valueindicative of the sufficiency of recovery associated with sequentialapneas and uses at least this value to define the disease severity insleep apnea.

It has been long believed that the number and duration of apneasdetermined the severity of disease. This basic concept seems intuitive.(i.e. If apnea are the detrimental events then it seems rational thatthe number and duration of the apneas would define severity). Perhapsbecause the severity issue seems so straightforward, the concept ofseverity as a function of enumeration of apneas has been promulgated fordecades and represents the standard of severity assessment in modernsleep medicine. However, the present inventor noted that with respect tobreath holding that severity cannot be determined by the number andlongevity of the breath holdings or by the magnitude of arousals oroxygen desaturation associated with the breath holdings, but rather isuniquely dependent on the sufficiency of the recovery interval betweenbreathholds. This is due to the fact that mammals (including humans)have an oxygen storage mechanism to protect against the stress ofbreatholding but this storage mechanism is readily depleted and must berepleted before the next breathhold. This means that, with respect toapnea, there is a unique and critical relationship betweencardiovascular stress associated with the sequential breathholds whichnaturally occur as a part of a self propagating apnea cluster and therecovery interval between breathholds. Indeed, this “sufficiency ofrecovery” between apneas is defined by a critical interaction betweenthe number of breathholds occurring in sequence, the longevity of thebreatholds, and the recovery time between the breatholds. As discussedsupra these relationships are provided by analyzing the apnea clusterwaveform. The present inventor has proposed that the diving sealprovides a reasonable analogy. The seal can dive very often and longwithout cardiovascular compromise as long as the animal has sufficientfree breathing recovery intervals above water between dives. When thisinterval is limited the seal faces a serious cardiovascular threat ifthe dives are frequent and prolonged. Indeed, in the wild, the need forsufficient recovery interval is exploited in the interest of predation.It is this unique relationship between severity and the sufficiency ofrecovery between sequential apneas which the present inventor hasutilized to provide a new system and method to determine the severity ofsleep apnea.

Upon this discovery the present inventor designed a system and method ofevaluating a patient with sleep apnea. The method includes identifying aplurality of sequential apneas, determining a value indicative of thesufficiency of recovery between the apneas, and determining the severityof sleep apnea based at least on said value. The value can bemeasurement such as the time interval or another measurement such as therelative amount of gas exhaled or inhaled from the mouth or nose betweenapneas. Both the interval time and the relative amount of gas exhaledcan be used in combination and another value indicative of thesufficiency of recovery can be calculated using this combination as forexample the product of the time and a measurement of the relative gasexhaled. The exhaled gas can be measured directly or inferred relativeto a baseline using a flow sensor (as is known in the art). Anothervalue indicative of the sufficiency of recovery is a measurementindicative of an oxygen saturation between apneas which can for examplebe expressed as the average oxygen saturation of the recovery interval.A presently preferred embodiment can include the steps of monitoring apatient to produce at least one timed waveform of at least onephysiologic parameter. The physiologic parameters can include forexample arterial oxygen saturation, the flow of gas at the nose and ormouth, (as can be measured by a thermister or a carbondioxide monitor asis known in the art), or chest wall movement. Then, the step ofidentifying along said waveform a first waveform variation indicative ofan apnea, then identifying along said waveform a second waveformvariation indicative of another apnea, then determining (as for exampleby measuring or calculating) the interval intermediate at least oneportion of said first waveform variation and at least one portion ofsaid second waveform, and finally assessing the severity of sleep apneabased on at least said determining.

An example of a waveform variation indicative of apnea is describedsupra for the oximetry waveform and can comprise a coupled desaturationand resaturation having characteristic slopes and occurring within adesaturation cluster as previously described. Preferably one portion ofthe first waveform variation corresponds to said one portion of thesecond waveform variation and it is further preferable that said oneportion of the first waveform variation is substantially the lastportion of said first waveform variation but the interval can extendinto each waveform variation as is described below for the intervaltermed the oxygen repletion interval where a portion of the secondwaveform variation is incorporated into the intervening interval. Oneportion of the second waveform variation can be substantially the firstportion of the second waveform variation.

The method can include identifying at least one cluster of waveformvariations indicative of a corresponding cluster of apneas wherein saidseverity assessment or determining is based on the position of saidwaveform variations within said cluster relative to other waveformvariations within said cluster. Alternatively or in combination aspatial cluster waveform pattern indicative of a spatial pattern of acorresponding cluster of apneas can be used to determine the spatialrelationships of said waveform variations within said cluster waveformpattern to determine the severity of sleep apnea.

The device for determining the severity of sleep apnea can comprise amonitor such as an oximeter or flow sensor capable of generating asignal indicative of at least one physiologic parameter and a processor(such as an integrated computer or a separate lap top computer) capableof processing said signal, said processor can operate to generate atimed waveform of said parameter and to identify a plurality ofsequential waveform variations indicative of a corresponding pluralityof sequential apneas, the sequential waveform variations have temporaland spatial relationships between said waveform variations and along thewaveform(as was discussed at length supra). The processor further canoperate to determine at least one of said temporal and said spatialrelationships and to display said result or determining so that saiddetermining can be used to assess the severity of sleep apnea.

Importantly the method of determining the severity of sleep apnea cancomprise the steps of identifying a plurality of sequential apneashaving a spatial relationship to each other, determining said spatialrelationship, and defining the severity of sleep apnea based on at leastsaid determining. The spatial relationship can be defined by an objectoriented method as discussed supra or by other known graphical methodswhich include pattern recognition or graphical event recognition.Alternatively or in combination with the methods noted above, a methodof determining the severity of sleep apnea can comprise steps ofidentifying a plurality of sequential apneas having a temporalrelationship to each other, determining said temporal relationship, anddefining the severity of sleep apnea based on at least said determining.The temporal relationship can be defined by an object oriented method asdiscussed supra or by other known methods such as frequency analysis orgraphical event recognition.

One preferred embodiment of a method to define the severity of sleepapnea is as follows:

Define a Desaturation Object as including two component objects:

1. An Initial Limb defined as that portion of the desaturation above aspecific threshold saturation (e.g. to a sat. of 80%) or as a percentageof the total fall in saturation (e.g. Forty percent of the total fall).

2. A Terminal Limb defined as that portion of the desaturation remainingafter the Initial Limb

Define a Positive Oscillation as a Resaturation followed by aDesaturation within a specific interval of less than 60 seconds.

Within each cluster, define a Repletion Interval as an object includinga Resaturation, a Plateau (the plateau may be absent) and a Initial Limbwithin a Positive Oscillation

Note: By extending this calculation through the initial limb thiscalculation takes into account the effect of an increased initialportion of the subsequent desaturation slope on the oxygen availabilityduring the recovery interval.

Calculate the average oxygen saturation during each Object as:$\overset{\_}{Sa02} = {\frac{1}{n}{\sum\limits_{t - n}^{n}{{Sa02}\left( {i\quad \Delta \quad t} \right)}}}$

Where: i=1 is the initial sample of the object, and i=1 is the finalsample, and Δt is the time interval between samples

With the object oriented program previously described the average oxygensaturation can comprise a characteristic of an object and as such can beeasily compared and plotted (for example, against duration of eachobject). This can for example, be applied to the repletion intervalswithin a given cluster. When plotted in this manner with saturation onthe y-axis and time on the x-axis a grouping of repletion intervalswithin the left lower quadrant of the plot is indicative of cluster witha low mean or median recovery interval in association with a low mean ormedian oxygen saturation during the recovery interval. This is generallyindicative of more severe sleep apnea but the indicator of severity isfurther enhanced and improved by incorporating the duration of theadjacent apneas such as the immediately bracketing apneas into the plotor calculation. The microprocessor system described supra can be used toassess and graphically present a severity analysis and to calculate aseverity index, which can be, derived as follows in a presentlypreferred embodiment.

1. PROGRAM—identifies the object cluster as defined above

2. PROGRAM—calculates the following for each cluster object:

1—Mean and median saturation (iterates through oxygen saturation values)

2—Mean and median apnea and cluster duration

3—Mean and median recovery interval (calculated as end of the nadir toonset of next desaturation.) and calculate avg. sat for each recoveryinterval and mean and median avg. saturations for said recoveryintervals

4—Mean and median maximum oxygen repletion interval (as mean recoveryinterval+40 percent of the next desaturation event) and calculate theavg. saturation and mean and median avg. saturations for this interval.

5—Plot the distribution of the duration for each repletion interval(x-axis) with either the average saturation for each repletion interval(y-axis) or the average duration of the apneas before and after eachrepletion interval (y-axis) (For example the durations of the two apneasimmediately before and two apneas immediately after the repletioninterval divided by 4). This plot is performed for each cluster objectand also performed as one plot in aggregate for all clusters.

6. Calculate the mean and median saturation of non clustered recording.

7. Calculate the time in minutes below 90%, 85%, 80%, and 70% duringcluster objects and during recording time wherein said objects are notpresent. Plot this as a comparison bar graph side by side (non clusteredbar for 90% adjacent clustered bar for 90% etc.) where the y axis ispercent of total recording total recording time and again on anothergraph where the y axis is time in minutes (the length of the y axis isequal to the total recording time)

8. Plot total cluster time and total non cluster time on a separategraph.

9. Calculate a value of the sufficiency of recovery as a sleep apneaseverity index termed the “Oxygen Repletion Index” (ORI) as the productof Oxygen Saturation minus 80 and the repletion interval. The ORI isgiven in “Saturation Seconds”. This can be calculated for each recoveryinterval and as a mean or median value for each cluster or portion of acluster (such as a portion of a cluster having a greater apnea durationor a greater magnitude of desaturation) or for the entire night.

10. Calculated another value of the sufficiency of recovery as anothersleep apnea severity index “Apnea Recovery Index” (ARI) as the quotientof the ORI and the mean duration of the apneas immediately bracketingeach recovery interval. The ARI is given in “Saturation Seconds perMinute of Apnea”. This also can be calculated for each recovery intervaland/or as a mean or median value for each cluster or portion of acluster (such as a portion of a cluster having a greater apnea durationor a grater magnitude of desaturation) or for the entire night.

As noted these sleep apnea severity indices can be calculated for theentire night as with the average index but is preferably visuallypresented to provide a more comprehensive picture of the heterogeneityof severity during a nights study. For this purpose the indices can beplotted for each cluster with each cluster as a new bar graph along atimed x-axis. The bar vertically presents the ORI with a zero linethrough the center. (Preferably the entire study time is plotted alongthe x-axis so that the width of the bar represents the duration of eachconsecutive cluster and the relative portion of the night spent withoutclustering is readily visible between the bars thereby allowing astraightforward visual presentation of the multiple parameters definingseverity as discussed supra.)

Illustrative Examples of ORI and ARI calculations:

A mean repletion interal saturation of 90% and a Repletion interval of20 seconds generates an ORI of 200 saturation seconds. If the bracketingapneas have a mean duration of two minutes this generates an ARI of 100saturation seconds per minute of apnea indicating mild severity.

A mean repletion interval saturation of 85% and a Repletion interval of20 seconds and a mean bracketing apnea duration of two minutes generatesan ARI of 50 saturation seconds per minute of apnea indicating a greaterdegree of severity.

A mean repletion interval saturation of 82% and a Repletion interval of20 seconds and a mean bracketing apnea duration of two minutes generatesan ARI of 20 saturation seconds per minute of apnea indicating verysevere disease.

A mean saturation of the repletion interval saturation of 78% and aRepletion interval of 20 seconds and a mean bracketing apnea duration oftwo minutes generates an ARI of −80 saturation seconds per minute ofapnea. (note here that in the event that the ORI is a negative numberthe mean duration of the bracketing apneas is multiplied times the ORIto generate the ARI). This level of ARI is indicative of profound, lifethreatening severity.

(Note that saturations below 80% will generate a negative ORI. A lowernegative ORI associated with increasing repletion times accounts for thefact that a long repletion time is actually reflects greater severitywhen the mean saturation of the repletion interval is profoundlydecreased since this indicates profound disease.)

Alternatively the index can be based on the recovery interval time andthe duration of the bracketing apneas without consideration of theoxygen saturation. These basic severity indices would be given inseconds per minute of apnea. Such severity indices are easy forphysicians to understand. For the purpose of defining the relativepotential for reduced oxygen delivery in the presence of criticalvascular stenosis these indices can be weighted to maximize the effectof lower saturation on oxygen delivery during the repletion interval.Additional weighting can be provided to incorporate the average durationof the clusters in combination with the mean recovery interval withinclusters which is another important value indicating sleep apneaseverity.

It is clear that alternative severity indices can be provided within thescope of the present invention. For example the number of breathsexhaled or inhaled between apneas can be used and indexed in combinationwith the mean duration of the bracketing apneas. Also, a wide range ofalternative aggregate severity indices of obstructive sleep apnea can beprovided utilizing the system and method of the present invention whichincorporate measurements or identification of events or deflectionsalong the waveform which correlate with the sufficiency of the recoveryintervals. Such severity indices can include in combination with therepletion intervals, or recovery intervals, the enumeration or frequencyevaluation of identified events or measurements indicative of apneaalong the waveform or can include waveform pattern identification whichprovides for the identification of grouped or closely spaced waveformdeflections which correlate with grouped or closely spaced apneas orapnea clusters having limited recovery intervals. As has been shown bythis teaching, with such a grouping, the limitation of the recoveryinterval may be inferred, for example, by a particular waveform patternof tightly grouped waveforms of high amplitude deflections.

In the preferred embodiment, severity is defined as inverselyproportional to the duration of the repletion intervals and the oxygensaturation of the repletion interval within the clusters and directlyproportional to the duration of the apneas within the cluster. Inaddition, the number of breaths, the relative magnitude of the breaths,the slope of the initial 50% of the descending limbs of thedesaturations within the cluster, and the duration of the clusters maybe all be incorporated to produce an aggregate index. Within the scopeof this teaching, alternative intervals can be used in place of theillustrative repletion intervals and recovery intervals described hereinand further additional intervals may be learned during the applicationof this teaching to clinical practice.

Although the presently preferred system and method for identifying thepresence of sleep apnea and for determining the severity of sleep apneahave been described, it will be obvious to those skilled in the art thatvarious modifications and changes may be made without departing from thescope of the invention. Therefore the claims are intended to include allsuch changes and modifications that fall within the true spirit andscope of the invention.

What is claimed is:
 1. A patient monitoring system for detectingventilation instability comprising: a pulse oximeter for repeatedlydetermining an arterial oxygen saturation value to generate a timeseries output of said arterial oxygen saturation values; and a processorprogrammed to detect the occurrence of a cluster of declines in saidvalues and to provide an output in response to the detection of saidoccurrence.
 2. The system of claim 1 wherein said occurrence comprises agrouping of closely spaced desaturations.
 3. The system of claim 1wherein said output is at least a visual output.
 4. The system of claim3 wherein said output is at least a textual output.
 5. The system ofclaim 4 wherein said textual output indicates at least one ofventilation instability and airway instability.
 6. The system of claim 1wherein said occurrence comprises a grouping of closely spaceddesaturations coupled with intervening resaturations.
 7. A patientmonitoring system for detecting ventilation instability comprising: apulse oximeter for repeatedly determining an arterial oxygen saturationvalue to generate a time series output of said arterial oxygensaturation values; and a processor programmed to detect at least oneoccurrence of at least one pattern of said values along said timeseries, said pattern being indicative of ventilation instability, saidpattern including at least a plurality of declines in said values alongsaid time series during a time period less than said time series, saidprocessor being further programmed to provide an output in response tosaid detection of said occurrence.
 8. The system of claim 7 wherein saidpattern comprises at least one grouping of closely spaced declines insaid values.
 9. The system of claim 7 wherein said output is comprisedof at least a visual output.
 10. The system of claim 7 wherein saidoutput is comprised of at least a textual output.
 11. The system ofclaim 10 wherein said textual output indicates at least one ofventilation instability and airway instability.
 12. The system of claim7 wherein said processor is further programmed to identify an occurrenceof at least one pattern of a plurality of rises in saturation.
 13. Aprocessing method for providing an automatic indication of ventilationinstability of a patient using a pulse oximeter, said pulse oximeterconnected with said patient and a processor programmed to detectdeclines in oxygen saturation values, the method including: (a)repeatedly determining an arterial oxygen saturation value of saidpatient to generate a time series output of said arterial oxygensaturation values; (b) detecting an occurrence of at least a pluralityof closely spaced declines in said values; and (c) providing an outputin response to said detecting.
 14. The method of claim 13 wherein saidoutput is at least a visual output.
 15. The method of claim 13 whereinsaid output is at least a textual output.
 16. The method of claim 13wherein said occurrence comprises a grouping of closely spaceddesaturations coupled with intervening resaturations.
 17. The method ofclaim 13 wherein said plurality of declines define at least one intervalbetween said declines, the method including determining at least oneinterval between said declines.
 18. A method for providing an indicationof ventilation instability of a patient using a processor and a pulseoximeter, said pulse oximeter connected with said patient, the methodincluding: (a) repeatedly determining the arterial oxygen saturationvalue of said patient to generate a time series output of said oxygensaturation values; and (b) using said processor, detecting an occurrenceof at least a plurality declines in said values wherein at least two ofsaid declines occur within a pre-selected interval; and (c) providing anoutput in response to said detecting.
 19. The method of claim 18 whereinsaid output is at least a visual output.
 20. The method of claim 18wherein said output is at least a textual output.
 21. The method ofclaim 20 wherein said textual output indicates at least one ofventilation instability and airway instability.
 22. The method of claim18 wherein said plurality of declines define at least one intervalbetween said declines, the method including determining at least oneinterval between said declines.
 23. The method of claim 18 wherein saidoccurrence comprises a grouping of closely spaced desaturations coupledwith intervening resaturations.
 24. The method of claim 18 wherein saidoccurrence defines a pattern of said plurality of declines along saidtime series, and wherein, said detecting comprises, at least, detectingsaid pattern.
 25. A patient monitoring system for detecting ventilationinstability comprising: a monitor for generating a time series of aoxygen saturation values and for determining a variation along said timeseries indicative of an occurrence of at least one of an apnea and ahypopnea, and, a processor programmed to detect an occurrence of acluster of said variations and to provide an output in response todetection of said occurrence.
 26. The system of claim 25 wherein saidmonitoring system comprises at least a pulse oximeter and wherein saidvariation comprises at least a fall in oxygen saturation.
 27. The systemof claim 25 wherein said monitoring system comprises at least a pulseoximeter and wherein said variation comprises at least a rise in oxygensaturation.
 28. The system of claim 25 wherein said occurrence of acluster of said variations comprises a grouping of closely spacedvariations.
 29. The system of claim 25 wherein said output is at least avisual output.
 30. The system of claim 25 wherein said variationcomprises at least a rise in oxygen saturation coupled to a fall insaturation.
 31. A microprocessor system for the evaluation of a patient,the system programmed to: (a) produce at least one time series of oxygensaturation values, (b) identify along said time series, a patternindicative of the occurrence of at least one of a plurality of closelyspaced apneas and a plurality of closely spaced hypopneas, and (c)output an indication based on said occurrence.
 32. A microprocessorsystem for the evaluation of a patient, the system programmed to: (a)produce at least one time series of at least arterial oxygen saturation,(b) identify along said time series, a pattern indicative of theoccurrence of a plurality of closely spaced falls in oxygen saturation,and (c) output an indication based on said occurrence.
 33. The system ofclaim 32 wherein said output is at least a textual output.
 34. Thesystem of claim 33 wherein said textual output indicates at least one ofventilation instability and airway instability.
 35. The system of claim32 wherein said pattern comprises a grouping of closely spaced falls inoxygen saturation, said falls, within said grouping being separated byrises in oxygen saturation.
 36. A patient monitoring system fordetecting ventilation instability comprising: a pulse oximeter forrepeatedly determining an arterial oxygen saturation value to generate atime series output of said arterial oxygen saturation values; and aprocessor programmed to recognize the occurrence of a cluster ofdeclines in said values and to provide an output in response to therecognition of said occurrence.
 37. The system of claim 36, wherein saidprocessor is further programmed to recognize a cluster pattern definedby a closely spaced grouping of declines separated by rises in saidvalues and wherein said pattern is indicative of an airway instability.38. The system of claim 36, wherein said processor is further programmedto recognize a plurality of clusters of declines.
 39. A patientmonitoring system for detecting ventilation instability comprising: apulse oximeter for repeatedly determining an arterial oxygen saturationvalue to generate a time series output of said arterial oxygensaturation values; and a processor programmed to recognize at least oneoccurrence of at least one pattern of said values along said timeseries, said pattern being indicative of ventilation instability, saidpattern comprising at least a plurality of declines in said valuesoccurring over a short time interval along said time series, saidprocessor being further programmed to provide an output in response tosaid recognition of said occurrence.
 40. The system of claim 39, whereinsaid short time interval is less than or equal to about 15 minutes. 41.The system of claim 39, wherein said short time interval is less than 10minutes.
 42. The system of claim 39, wherein said pattern is comprisedof a cluster of declines separated by rises in saturation values bothsaid declines and said rises occurring within said short time interval.43. The system of claim 39, wherein said pattern is comprised of threeor more separate declines occurring within said short time interval. 44.The system of claim 39, wherein said pattern is comprised of three ormore declines each separated by rises in saturation values, both saiddeclines and said rises occurring within said short time interval. 45.The system of claim 39, wherein said processor is further programmed torecognize a pattern of closely-spaced declines separated by rises, andwherein said pattern is indicative of airway instability.
 46. The systemof claim 39, wherein processor is further programmed to recognize apattern of closely spaced declines separated by rises, and wherein saidpattern is indicative of obstructive sleep apnea.
 47. A method forproviding automatic indication of ventilation instability of a patientpromptly upon an occurrence of said ventilation instability using aprocessor and a pulse oximeter without the need for attendance by aperson, said pulse oximeter connected with said patient, the methodincluding: (a) repeatedly determining an arterial oxygen saturationvalue of said patient to generate a time series output of said arterialoxygen saturation values; and (b) using said processor, recognizing anoccurrence of at least a plurality of declines in said values, whereinat least two of said declines occur within a preselected interval; and(c) providing an automatic output in response to said recognizing. 48.The method of claim 47, wherein said recognition comprises recognitionof an occurrence of a pattern of closely spaced declines and whereinsaid pattern is indicative of an airway instability.
 49. The method ofclaim 47, wherein recognition comprises recognition of an occurrence ofa pattern of closely spaced declines and wherein said pattern isindicative of a sleep apnea.
 50. The method of claim 47, wherein saidrecognition further comprises recognition of an occurrence of a patternof declines and rises in saturation values and wherein at least two ofboth said declines and said rises occur within a short time interval andwherein said pattern is indicative of airway instability.
 51. The methodof claim 47, wherein said time interval is less than 10 minutes.
 52. Themethod of claim 47, wherein said recognition further comprisesrecognition of an occurrence of a pattern closely spaced falls occurringwithin a time interval of less than 10 minutes.
 53. The method of claim47, wherein said recognition further comprises recognition of anoccurrence of a pattern indicative of obstructive sleep apnea.
 54. Apatient monitoring system for automatically detecting ventilationinstability without need for attendance by a person comprising: a pulseoximeter for repeatedly determining an arterial oxygen saturation valueto generate a time series output of said arterial oxygen saturationvalues; and a processor programmed to automatically identify as abnormalan occurrence of at least one of a plurality of closely-spaced declinesand a plurality of closely-spaced rises in said values, wherein saidoccurrence occurs over a short time interval, and to provide anautomatic output in response to said identification, said outputincluding a rapid response to said identification so that patienttreatment can be quickly adjusted in response to said occurrence. 55.The system of claim 54, wherein said processor is further programmed toautomatically adjust patient treatment in response to said occurrence.56. The system of claim 54, wherein said short time interval is lessthan or equal to about 15 minutes.
 57. The system of claim 54, whereinsaid short time interval is less than 10 minutes.
 58. The system ofclaim 54, wherein said processor is further programmed to automaticallyidentify as abnormal an occurrence of a plurality of closely-spaceddeclines separated by rises, wherein a mean absolute value of a slope ofthe rises is about equal to or greater than a mean absolute slope of thedeclines.
 59. The system of claim 54, wherein said processor is furtherprogrammed to automatically identify as abnormal an occurrence of aplurality of closely-spaced declines separated by rises, wherein anabsolute magnitude of the rises is about equal to or greater in than amagnitude of the declines.